Documents published in 2026 : Scipedia

Constructability‑based multi‑objective optimization with machine learning‑enhanced meta‑heuristics for reinforcing bar design in rectangular concrete columns
L. Verduzco, L. Verduzco Martínez

Constructability‑based multi‑objective optimization with machine learning‑enhanced meta‑heuristics for reinforcing bar design in rectangular concrete columns (2025). Structural & Multidisciplinary Optimization, 68(29)

Abstract
Optimization of reinforcing bar (rebar) design represents a preponderant factor in reducing material usage and wastes for reinforced concrete (RC) structures. The assessment of constructability of such rebar designs is crucial to improve their practicality and reduce construction costs, which makes the problem multi-objective (MO). However, when applying optimization methods for the design of rebar in RC structures, little attention has been paid on columns, in comparison to beams and slabs. Meta-heuristic algorithms (MA) have been the ones mostly deployed for these types of elements, which have proven to be of high computational cost. Additionally, an existing gap in the literature as to how to relate the design and construction stage of rebar in RC structures through constructability analysis is evident. In this regard, research has been focused mainly at the building level but not at the element level. This works presents a novel algorithmic framework using Machine Learning (ML)-enhanced meta-heuristics for the optimal design of rebar on rectangular RC columns. To assess the constructability of the resulting rebar layouts a Buildability Score (BS) model at the element level is proposed. The complexity analysis of rebar design under the constructability restrictions, through combinatorial optimization (CO), is used to assess the global time efficiency of the framework. The Non-Sorting Genetic Algorithm II (NSGA-II) was deployed for showcase and five different ML algorithms were used to enhance it, namely the k-NN classifier, SVM regression, ANN, Gauss Process (GP) regression, and Tree Ensembles (TE), where the latter three showed the best performance.
Abstract
Optimization of reinforcing bar (rebar) design represents a preponderant factor in reducing material usage and wastes for reinforced concrete (RC) structures. The assessment [...]
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CALRECOD -- A software for Computed Aided Learning of REinforced COncrete structural Design
L. Verduzco Martínez

CALRECOD -- A software for Computed Aided Learning of REinforced COncrete structural Design (2022). Advances in Engineering Software 172

Abstract
It is presented the development and implementation of a new computed aided learning MatLab Toolbox for the design of reinforced concrete structures named as CALRECOD for their abbreviation \textit{Computer Aided Learning of Reinforced Concrete Design}. Such development emerges as the result of a series of research works in the Autonomous University of Queretaro with the main purpose of improving the way in which the design of reinforced concrete structures is taught in high education institutions. CALRECOD uses optimization methods and algorithms to aid students in their design interaction learning so that they are able to compare their own designs and what commercial software delivers with optimal ones given certain load conditions on the elements or structures. The software consists almost entirely of MatLab functions (.m files) and the ACI 318-19 code is taken as their main design reference to make it internationally useful, although in some cases the Mexican code NTC-17 specifications are used. Besides MatLab functions, the software consists as well of ANSYS SpaceClaim script functions (.scscript files) as an additional tool for the aid in the visualization of design results in a 3D space in the software ANSYS SpaceClaim. CALRECOD has proven to be versatile, flexible and of easy use with a huge potential to increase learning outcomes for students in high education programs related with the design of reinforced concrete structures as well as to enhance the creation of efficient interactive environments for researchers and academics focused on the development of new design and analysis methods for such structures. With their optimization design functions, a solid comparison platform of designs' performance could be laid out and with its extended function design packages for structural systems, reinforced concrete design courses could be enhanced in a great deal regarding their program content's scope. The software can be found at:{https://github.com/calrecod/CALRECOD
Abstract
It is presented the development and implementation of a new computed aided learning MatLab Toolbox for the design of reinforced concrete structures named as CALRECOD for [...]
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Outlier-Resistant Neutrosophic Ratio Estimators based on Generalized M-Estimators
K. Abuhasel

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 65

Abstract
Building efficient ratio-type estimators of population parameters, especially the mean and variance, has been a major theme in sampling theory. However, the growing frequency of dirty, inaccurate, and incomplete information still poses a threat to the credibility of the classic estimation processes, especially in environments where outliers are likely to occur. The paper derives a generalized type of neutrosophic robust ratio-type estimator and regression-type estimators that have been developed on the M-estimation platform, including Huber M-estimators and generalized M-estimators (Viz., Mallows-GM, Schweppes-GM, and SIS-GM) formulations, and also incorporating the auxiliary information of the HodgesLehmann estimator. The estimators are designed to be asymptotically efficient in clean data models and apply well to contamination and heavytailed error distributions. Through a comparative study using real-world data and a Monte Carlo simulation experiment, it is shown that the proposed estimators show better performance, numerical stability, and robustness compared to the current methods in the presence of uncertainty. The simulation results confirm their resilience to contamination and heavy-tailed distributions across varying contamination levels. An application to environmental data involving temperature measurements subject to measurement error and outliers further illustrates the practical relevance of the framework. Collectively, the empirical and simulation evidence support the applicability of the proposed methodology to industrial process analysis and environmental monitoring systems characterized by data imprecision and contamination.OPEN ACCESS Received: 16/11/2025 Accepted: 07/01/2026
Abstract
Building efficient ratio-type estimators of population parameters, especially the mean and variance, has been a major theme in sampling theory. However, the growing frequency [...]
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Integration of Monte Carlo Simulation and Linear Programming in Multi-Product Multi-Stage Supply Chains: A Theoretical Framework for Decision-Making under Uncertainty
K. Abuhasel

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 64

Abstract
This study outlines a hybrid Monte Carlo Simulation-Linear Programming framework to increase the level of operational efficiency of a multistage supply chain. This model is an integration of probabilistic simulation and deterministic optimization to take into account the effects of demand variability, lead-time variability, and capacity variability on profitability and the overall service delivery performance. The suggested framework is tested with the help of a multi-product, multi-stage supply chain case study that is implemented on the basis of a publicly available dataset containing around 9000 transactional records. Monte Carlo simulation generates random uncertainty scenarios, whereas linear programming finds the ideal decisions related to production, distribution, and inventory levels for each iteration. The results suggest that reducing the amount of demand variability and better capacity planning resulted in a good performance with an expected profit of $328,100.16, a profit variance of 3.72× 109, and a service level of 94.3%. The result of the sensitivity analysis shows that demand variability and lead times have a negative effect on profit, while optimal capacity planning enhances operational flexibility. The MCS-LP provides an advantage to the use of stochastic and deterministic methods for risk-aware decision making and has the potential to be computationally scalable and efficient for uncertaintydriven supply chain design. The general approach provides a decisionsupport tool for managers considering how to balance costs, risk, service quality, and uncertainty in ever-changing industrial contexts.OPEN ACCESS Received: 14/11/2025 Accepted: 13/01/2026
Abstract
This study outlines a hybrid Monte Carlo Simulation-Linear Programming framework to increase the level of operational efficiency of a multistage supply chain. This model is [...]
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Concurrent Execution Process Modeling and Interaction Strategy among Coupled Design Tasks under EPC Mode
L. Wang

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 63

Abstract
The concurrent execution of preliminary design and construction drawing design under the Engineering Procurement Construction (EPC) mode is an important method to achieve efficient connection in technology and deep integration in management between them, but it also faces the risk of multiple rework and information transmission caused by repeated iterative coupling and frequent information interaction. This issue leads to inefficiencies and increases project risk, particularly in managing interdependencies and mitigating rework. Unlike previous studies, this research focuses on optimizing the interaction strategies to minimize these risks and improve overall project performance in EPC projects. In view of this, on the basis of planning and quantifying the parallel execution process of preliminary design and construction drawing design, and constructing the corresponding change probability function (which is a model that predicts the likelihood of design information changes), this study introduces a novel analytical framework for predicting the likelihood of information changes and calculating their impact on task durations. A parallel execution duration decision-making model for solving the optimal information interaction strategy (the method of managing communication and decision-making to reduce delays and rework) is constructed. This model offers new decision guidelines to minimize rework and reduce delays in design tasks. Subsequently, through the numerical derivation and analysis of the change probability function, seven propositions about the interaction strategy are proposed. Through the numerical solution and analysis of the parallel execution duration model, six propositions about the interaction strategy are proposed. These propositions contribute to theory by offering a model that quantifies rework and information transfer in concurrent execution, and to practice by providing actionable insights for improving EPC project management. The results provide methodological countermeasures for discussing the interaction strategy of preliminary design and construction drawing design in the concurrent execution process from the perspective of quantitative analysis, and also provide directional suggestions for the application method of concurrent engineering concept in EPC mode.OPEN ACCESS Received: 09/08/2025 Accepted: 13/01/2026
Abstract
The concurrent execution of preliminary design and construction drawing design under the Engineering Procurement Construction (EPC) mode is an important method to achieve [...]
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Garhy distribution with Different Estimation Methods and Applications to Engineering and Medical Data
M. Elgarhy

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 61

Abstract
In this article, we introduce and investigate a new one-parameter mixture distribution called the “Garhy distribution”. The probability density function is very adaptable, as it may take on right skewed, unimodal, and heavy tailed patterns. In addition, the hazard rate function indicates that data with increasing shaped failure rates may be adapted by the Garhy distribution. Several fundamental statistical and mathematical properties are calculated including mode, quantile function, moments, mean, variance, skewness, kurtosis, moment-generating function, incomplete moments, inequality measures, order statistics, and extropy measures. The scale parameter of the Garhy distribution is estimated employing twelve different estimation approaches, maximum likelihood, maximum product of spacings, least-squares, weighted least-squares, Anderson darling, right-tail Anderson darling, left-tail Anderson darling, Cramér von-Misses, and the least-squares method. The effectiveness of these strategies is evaluated using a detailed simulation study. Furthermore, we used the Garhy distribution to examine two real-world data sets, demonstrating its superior performance compared to specific competitors.
Abstract
In this article, we introduce and investigate a new one-parameter mixture distribution called the “Garhy distribution”. The probability density function is very [...]
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Advancing Fractional Calculus: A Fixed-Point Strategy for Nonlinear Caputo-Hadamard Equations
M. Awadalla, M. MURUGESAN

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 62

Abstract
This paper investigates the existence, uniqueness, and Ulam–Hyers stability of solutions for a coupled system of nonlinear Caputo–Hadamard fractional differential equations in Banach spaces. By reformulating the boundary value problem into an equivalent integral system via the Hadamard fractional integral operator, sufficient conditions for existence and uniqueness are established using Krasnoselskii’s and Banach’s fixedpoint theorems. Within the same functional framework, Ulam–Hyers stability results are derived for the proposed system. The theoretical analysis provides a consistent and unified approach for studying nonlinear coupled fractional systems with nonlocal operators, and the validity of the assumptions is illustrated through representative examples.
Abstract
This paper investigates the existence, uniqueness, and Ulam–Hyers stability of solutions for a coupled system of nonlinear Caputo–Hadamard fractional differential [...]
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List of participants
Q. Phung

vialb (2026). 5
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WORKSHOP AGENDA
Q. Phung

vialb (2026). 4
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Book of Abstracts
Q. Phung

vialb (2026). 3
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Presentation to Eugenio Oñate and others of the National Research Award 2024 by the King of Spain Felipe VI.
E. Oñate

eov (2026). 5
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Tribute of CIMNE to Eugenio Oñate on his retirement
E. Oñate

eov (2026). 4
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Proposing A Novel Deep Learning And Clustering-Based Method For Autism Detection In Brain Mri Images
Z. Alqazzaz, O. Alhussein, S. Abidemi, S. Alturjman, F. Al-Turjman

Osman Alhussein's personal collection (2026). 1

Abstract
Autism Spectrum Disorder (ASD) represents a complicated neurodevelopmental condition where early diagnosis is imperative for effective intervention. Behavior assessments, which are somewhat the norm, primarily slow down diagnosis. This thesis presents a hybrid deep learning approach using brain MRI data for the early detection of autism. This approach combines FCM and GMM clustering MRI data, the multi-channel deep learning ResNet architecture for image classification, and the Dwarf Mongoose Optimization Algorithm for feature enhancement, model refinement, and improving generalization. The proposed model achieved an accuracy of 93.72%, precision of 94.81%, recall of 93.14%, and an F1-score of 93.97% when evaluated on the ABIDE I dataset. These results demonstrate and give evidence of the model overcoming challenges related to previously insufficient models in overfitting and predictive performance. This research is geared towards helping automate feature extraction on highly dimensional MRI data having a great scope for ASD clinical diagnosis. This can benefit ASD clinical diagnosis and serve as a starting point for research in advanced ASD clinical diagnosis.
Abstract
Autism Spectrum Disorder (ASD) represents a complicated neurodevelopmental condition where early diagnosis is imperative for effective intervention. Behavior assessments, [...]
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Video on the Particle Finite Element method
E. Oñate

eov (2026). 3

Abstract
The video presents the theoretical foundations of the Particle Finite Element Method (PFEM, https://cimne.com/pfem-launches-its-new-website/ ) and its applications in engineering and other applied sciences.
Abstract
The video presents the theoretical foundations of the Particle Finite Element Method (PFEM, https://cimne.com/pfem-launches-its-new-website/ ) and its applications in [...]
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Video on the occasion of the 2024 National Research Award in Engineering and Architecture
E. Oñate

eov (2026). 2

Abstract
The video contains the interview that the Ministry of Science, Innovation and Universities conducted with Eugenio Oñate on the occasion of the awarding of the 2024 National Research Prize, Torres Quevedo category in Engineering and Architecture.
Abstract
The video contains the interview that the Ministry of Science, Innovation and Universities conducted with Eugenio Oñate on the occasion of the awarding of the 2024 [...]
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Methodological Approach for Understanding Particle Transport. Case Study: Analyzing Influential Factors and Comparing Simulation Software
C. Müssing, A. Kabat vel Job, S. Knorr, H. Meyer, D. Wiest

Particles 2025.

Abstract
Recent research shows that the investigation of particle flow of small particles with diameters smaller than 10 micrometers has emerged as an important field of interest, especially for purposes in the industrial sector. Investigating the behavior, deposition and acting forces of individual particles have become particularly relevant. Understanding theses dynamics is crucial for setting up practical simulations in terms of particle transport drawn from industrial problems. Previous research has identified numerous forces involved in particle transport e.g. Sommerfeld, 2000, Crown, 2005, Löffler & Raasch, 1992 or Crown, et al., 2011. However, not all of the various forces have yet been implemented in practice for different reasons. In this case study, acting forces are examined theoretically using a component coming directly from the automotive industry. The examined component is a venting tube used in headlights of vehicles. Due to its simple geometry the component is suitable for investigating particle-laden flow using different simulation software. The simulation setup for this component is less complex, which saves time and reduces computational costs. The implementation of the examination processes within different types of simulation software has been analyzed too. Simulating a particle-laden flow results in significant computational costs. It is therefore necessary to identify and implement boundary conditions. All results serve as the basis for developing an appropriate simulation model and to create a methodological approach representing particle transport. The simulation results have been validated using suitable experiments. Furthermore, a transfer function is derived to determine the filtering effect of the mentioned automotive component. This study combines theoretical background with a practical approach.
Abstract
Recent research shows that the investigation of particle flow of small particles with diameters smaller than 10 micrometers has emerged as an important field of interest, [...]
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On a Class of Hybrid Langevin Inclusions Involving Two Generalized Fractional Derivatives
A. Lachouri, M. Arab

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 60

Abstract
In this paper, we study the existence of solutions for a hybrid Langevin inclusion involving a combination of φ-Hilfer and φ-Caputo fractional derivatives. To this end, we construct a new operator derived from the integral solution of the given boundary value inclusion problem and subsequently apply the hypotheses of Dhage’s fixed point theorem to this fractional operator. Finally, to support our theoretical findings, an illustrative example is provided.OPEN ACCESS Received: 13/11/2025 Accepted: 20/01/2026
Abstract
In this paper, we study the existence of solutions for a hybrid Langevin inclusion involving a combination of φ-Hilfer and φ-Caputo fractional derivatives. To this [...]
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Integrability, Conservation Laws by Variational Analysis and Lump Solitons for (2+1) Fourth-Order Biharmonic Equations with Quantum Field Applications
B. Alqurashi, A. Raza

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 59

Abstract
We study the integrability via conservation laws and discuss the nonlinearity of the fourth-order biharmonic equations in (2+ 1) dimensions related to quantum field models based on the potential functions h(u). Lie symmetry reduction is performed, and the forms of the invariant solutions are presented, including travelling wave solutions. Variational analysis has been performed based on the various potential functions h(u). Corresponding Euler-Lagrange equations and conservation laws are investigated by Noether’s theorem and presented in the form of conserved vectors. The obtained conserved flows define energy, momentum and flow dynamics supporting the system integrability. Furthermore, detailed lump and breather solutions are presented for each potential h(u) using Bilinear forms illustrating various localized and oscillatory field characteristics.OPEN ACCESS Received: 08/11/2025 Accepted: 17/12/2025
Abstract
We study the integrability via conservation laws and discuss the nonlinearity of the fourth-order biharmonic equations in (2+ 1) dimensions related to quantum field models [...]
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Monitoring Techniques for Structural Health of Roads Using Emerging Technologies: A Systematic Re-View of the Last Five Years
A. Quezada, Y. Huaricallo

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 58

Abstract
This systematic review examines the most recent techniques used to monitor the structural health of roadways, with a special focus on emerging technologies applied over the past five years. The progressive deterioration of road networks and the limitations of traditional inspection methods have driven the development of more precise, automated, and efficient solutions. The technologies analyzed include LiDAR laser scanning, drones equipped with computer vision, visual sensors, mobile cameras, ground-penetrating radar (GPR), and unmanned aerial vehicles (UAVs). Each technique was assessed based on its accuracy and the type of pavement distress it can identify, such as cracks, potholes, and surface deformations. The findings indicate that these tools enhance the efficiency and safety of inspections, enabling real-time data collection. Additionally, there is a growing trend toward the integration of artificial intelligence algorithms to automate data analysis. However, data heterogeneity and the need for cross-domain model adaptation may affect performance and scalability in large-scale or multi-source scenarios. Overall, this study provides an updated perspective on the application of emerging technologies in road infrastructure management, contributing to the development of innovative strategies for sustainable and intelligent roadway maintenance.OPEN ACCESS Received: 09/09/2025 Accepted: 09/12/2025 The search strategy was designed based on the PICOC framework, using the following Figure 1: Most frequent and trending
Abstract
This systematic review examines the most recent techniques used to monitor the structural health of roadways, with a special focus on emerging technologies applied over the [...]
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Smart Sensors and Artificial Intelligence for Urban Water Networks: A Systematic Literature Review (2015–2025)
N. Cruz, A. Cuba, Y. Huaricallo

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 57

Abstract
Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol, covering research published between 2015 and 2025 on smart sensors and advanced techniques for leak detection and consumption optimization. From 788 initial records, 40 studies met the inclusion criteria. Findings indicate that acoustic, pressure, fiber-optic, and hybrid sensing enable real-time monitoring and accurate leak localization, with typical error margins between±1% and ±5%, depending on sensor type and hydraulic conditions. A marked shift toward artificial intelligence (AI) and machine learning is observed for optimal sensor placement, event classification, and prediction, achieving >95% accuracy. The cost analysis reveals a direct relationship between technological sophistication and required investment. Overall, integrating smart sensors with AI provides a promising pathway toward more sustainable, efficient, and resilient urban water management.OPEN ACCESS Received: 06/09/2025 Accepted: 30/10/2025 The search strategy was constructed using combinations of keywords and Boolean operators, adjusted for each database to ensure consistency and reproducibility. The searches targeted titles, abstracts, and
Abstract
Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic [...]
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Applications of Artificial Intelligence in Bridge Structural Health Monitoring: A Systematic Review of the Last Decade
B. Silva, L. Vega, Y. Huaricallo

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 56

Abstract
Bridges are critical components of transportation networks whose structural integrity is often threatened by aging, environmental loads, and insufficient maintenance. Structural Health Monitoring (SHM) supported by Artificial Intelligence (AI) offers a transformative approach to early damage detection, predictive maintenance, and operational safety. This study presents a systematic literature review, conducted in accordance with PRISMA 2020 guidelines, on the application of AI algorithms to bridge SHM between 2015 and 2025. A total of 70 peer reviewed articles were analyzed, covering diverse geographic contexts, structural types, and sensing technologies. The review categorizes studies by AI technique (ANN, CNN, LSTM, SVM, hybrid models, and emerging methods such as Transformers and Graph Neural Networks), sensor architecture (accelerometers, fiber optic sensors, UAV based imaging, IoT modules), and performance metrics. Results indicate that convolutional and recurrent neural networks achieve detection accuracies above 95% and R2values exceeding 0.90 in displacement prediction, while hybrid approaches combining deep learning with traditional classifiers enhance robustness. Sensor integration with IoT and multimodal data fusion improves detection sensitivity, with correlation values above 0.99 in some cases. However, over 90% of studies lack robust cross validation, real world deployment, or standardized performance reporting, limiting replicability. This review highlights current trends, technical challenges, and research opportunities, including the need for interoperable sensor– algorithm platforms, explainable AI models, and broader implementation in developing regions. By consolidating existing knowledge, the study provides a technical reference for researchers, practitioners, and policymakers aiming to implement intelligent, predictive, and resource efficient bridge SHM systems.OPEN ACCESS Received: 27/08/2025 Accepted: 13/11/2025
Abstract
Bridges are critical components of transportation networks whose structural integrity is often threatened by aging, environmental loads, and insufficient maintenance. Structural [...]
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The Particle Finite Element Method PFEM in engineering
E. Oñate

eov (2026). 1
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Study on Step-Stress Accelerated Life Testing for the TDUSW Distribution Using the Cumulative Exposure Model Under Progressive Type-II Censoring
F. Riad, S. Mubarak, E. Almetwally

Fathy Hamdi's personal collection (2026). 2

Abstract
This work investigates a simple step-stress accelerated life testing plan based on progressive Type-II censoring for units whose lifetimes follow the TDUSW distribution. The cumulative exposure model is adopted to describe the effect of changing stress levels during the experiment. Under this framework, the probability density function and the cumulative distribution function of the TDUSW distribution are incorporated into the likelihood structure of the progressively censored sample. Maximum likelihood estimators of the distribution parameters are derived, and numerical procedures are suggested to obtain their estimates. In addition, the asymptotic variance–covariance matrix is computed via the observed Fisher information. A real data example and a simulation study are presented to illustrate the performance of the estimators under various censoring schemes. The results demonstrate that the TDUSW model provides a flexible structure for modeling accelerated life-testing data and yields stable parameter estimates even under moderate censoring.
Abstract
This work investigates a simple step-stress accelerated life testing plan based on progressive Type-II censoring for units whose lifetimes follow the TDUSW distribution. The [...]
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How Can an Appropriate CFD Model be Developed for Turbulent Flow in Rough Pipes?: Evidence from Friction Factor Prediction
K. Boonsamer, B. Temsiriphan, P. Thongnoi, S. Areerat, E. Bumrungthaichaichan, S. Wattananusorn

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 55

Abstract
This paper answers the question: “How can an appropriate turbulent rough pipe flow computational fluid dynamics (CFD) model be developed?” The Reynolds-averaged Navier-Stokes equations with the standard k-epsilon turbulence model and scalable wall functions were solved to obtain Fanning friction factors and mean velocity profiles in inflectional and monotonic rough pipes. CFD models with near-wall grid sizes from four dimensionless wall distances and two roughness treatment approaches were simulated. Eight roughness Reynolds numbers, covering the lower end of the transitionally rough regime through the fully rough regime, were studied for each roughness type. Appropriate roughness and turbulence model constants for turbulent rough pipe flows in the transitionally rough regime were determined. For model validation, the predicted mean axial velocity profiles for Reynolds numbers of 5 × 104and 5 × 105exhibited good agreement with the reference experimental data. A total of 208 CFD simulations (32 from our previous works and 176 from the present study) were analyzed. Finally, based on comparisons between predicted Fanning friction factors and established correlations, appropriate CFD models for turbulent flows in inflectional and monotonic rough pipes were identified. Suitable CFD models for accurately predicting mean velocity profiles at roughness Reynolds numbers below 11.225 were also obtained, although with the caution that improved mean velocity prediction may reduce Fanning friction factor accuracy. Furthermore, the present CFD work provides essential guidance for extending simulations to other rough surface types and rough-wall flow situations.OPEN ACCESS Received: 25/11/2025 Accepted: 30/12/2025
Abstract
This paper answers the question: “How can an appropriate turbulent rough pipe flow computational fluid dynamics (CFD) model be developed?” The Reynolds-averaged [...]
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Computational Investigation of Fractal-Fractional Nonlinear Viscoelastic Fluids Using Local Radial Basis Function Method
H. Ahmad

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 54

Abstract
Fractal-fractional derivatives generalize both traditional and fractional differentiation approaches by integrating memory effects with fractal properties. This mathematical framework is especially valuable for describing complex systems in which conventional continuum mechanics becomes inadequate, particularly in scenarios involving porous or discontinuous structures. This research investigates the behavior of a non-linearWalter’s-B fluid subjected to time-varying thermal and concentration conditions. Beyond the extended derivative formulation, the analysis incorporates phenomena including first-order chemical reactions, radiative heat transfer, Joule heating, Soret effect, and viscous dissipation. Thesystem is also subjected to a transverse magnetic field with magnitude B0.The fluidmodel is initially formulated through traditional constitutive equations and subsequently generalized using a fractal-fractional operator. Solutions to this extendedmodel are computed employing ameshfree numerical approach utilizing localized radial basis functions (LRBF), which eliminates the requirement for structured grids and improves precision when addressing intricate geometries.The computational outcomes, displayed through graphical representations, illustrate how the fractional and fractal parameters influence the rheological characteristics of the Walter’s-B fluid. These findings establish that adjusting these parameters enables retrieval of classical, fractional, and fractal formulations as particular instances within this comprehensive mathematical structure.
Abstract
Fractal-fractional derivatives generalize both traditional and fractional differentiation approaches by integrating memory effects with fractal properties. This mathematical [...]
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Multisensor Fault Diagnosis Leveraging Reinforced Evidential Jensen-Alpha Divergence under Dempster-Shafer Theory
Z. Liu, J. Liao, L. Wang, Y. Huang, K. Liu, H. Dhumras, W. Mbasso

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 53

Abstract
Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis. Dempster-Shafer theory (DST) has garnered significant interest across various fields as it provides a powerful framework for modeling uncertainty. However, despite its advantages, the application of Dempster’s rule can lead to paradoxical outcomes when it encounters highly conflicting evidence. To address this limitation, this paper first presents a new evidential Jensen-alpha divergence (EJ AD) to quantify the discrepancy between the evidence items based on DST. Furthermore, an advanced version, the reinforced evidential Jensen-alpha divergence (REJ AD) is developed, which takes into account the quantity of potential propositions. We demonstrate thatREJ ADcan be transformed into various divergences such as the χ2divergence, Jensen-Shannon divergence, Hellinger distance, and arithmetic-geometric divergence under certain conditions. Also, we show the key properties ofREJ AD, including non-negativity, non-degeneracy and symmetry. Additionally, we design a new multisensor fault diagnosis method utilizingREJ ADand belief entropy. The superior performance of the proposed method is tested in three distinct fault diagnosis cases, and analysis shows robust performance across a range of its key parameter α, offering a computationally feasible, scalable and interpretable solution for AI-based decision-making in real-world engineering applications.OPEN ACCESS Received: 15/10/2025 Accepted: 17/11/2025
Abstract
Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis. Dempster-Shafer [...]
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A Hybrid Approach for Vulkan-Based Ray Tracing Implementations
M. Kim, N. Baek

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 49

Abstract
Three-dimensional (3D) graphics output started with traditional local shading models processed in real time. Ray-tracing techniques are actively introduced for high-quality output. These traditional local shading pipelines and ray tracing pipelines are typically provided independently. Recently, hybrid rendering methods were introduced to integrate the results of these pipelines for better real-time graphics results. However, technical problems arise with heterogeneous application programming interfaces (APIs). Vulkan was recently introduced as a new low-level graphics API in practical 3D graphics implementations. With its new ray tracing extensions, Vulkan has become one of the 3D graphics environments that simultaneously supports traditional local shading and modern ray tracing pipelines. Stand-alone ray tracing and hybrid rendering techniques are implemented to determine a practical implementation of the ray tracing technique in this latest Vulkan environment. This work reveals the completeness of both implementations and compares the graphics performance with experimental computer animation sequences and different camera configurations. The hybrid rendering techniques perform better with previous graphics processing unit models, and the core ray tracing implementation achieves faster processing speeds of 39% to 64% compared to the stand-alone ray tracing method, at least in the Vulkan environment with the latest NVIDIA graphics card. In contrast, the preprocessing of the geometry pass takes noticeable time, which reduces our overall performance improvements. The experimental results can be applied as guidelines for implementing practical real-time 3D graphics applications and as a new benchmark model for ray tracing implementations.OPEN ACCESS Received: 23/08/2025 Accepted: 28/11/2025 Published: 03/02/2026
Abstract
Three-dimensional (3D) graphics output started with traditional local shading models processed in real time. Ray-tracing techniques are actively introduced for high-quality [...]
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Large Language Model-Driven Demand Forecasting and Inventory Optimization for University Physical Education Resource Supply Chain
J. Ma, L. Liu, S. Liu, H. Wang

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 50

Abstract
This study proposes an intelligent management system for university physical education resource supply chains based on Large Language Models (LLMs), aiming to address the problems of inaccurate demand forecasting and inefficient inventory management in traditional physical education resource allocation. By constructing a deep learning framework incorporating LLMs and combining multi-dimensional information including historical data, seasonal factors, course schedules, and student preferences, precise demand forecasting for sports equipment, facilities, and teaching resources is achieved. The research employs a pre-trained language model based on the Transformer architecture, combined with time series analysis and reinforcement learning algorithms, to develop dynamic inventory optimization strategies. Experimental results demonstrate that compared to traditional methods, this system improves demand forecasting accuracy by 23.7%, increases inventory turnover rate by 31.2%, and achieves a resource utilization rate of 89.6%. This research provides a novel solution for intelligent management of university physical education resources, offering significant theoretical value and practical implications.OPEN ACCESS Received: 24/08/2025 Accepted: 28/10/2025 Published: 03/02/2026
Abstract
This study proposes an intelligent management system for university physical education resource supply chains based on Large Language Models (LLMs), aiming to address the [...]
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Prediction of Entropy Production and Heat Transfer Characteristics of Al2O3-Cu/Water Hybrid Nanofluid in Convection-Radiation Interaction Flow in a Porous Cavity byMachine Learning Approach
A. Çolak

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 48

Abstract
Minimizing entropy production is critically important, particularly in nanofluid flows. Applying this principle to flows with porous cavity structures helps optimize heat transfer applications and enhance system efficiency. In this study, the entropy production and heat transfer characteristics of a hybrid nanofluid composed of Al2O3-Cu particles suspended in water were investigated using machine learning. The nanofluid was analyzed in the context of convection–radiation interaction flow within a porous cavity.An artificial neural networkmodel was developed to predict the average Nusselt number, Bejan number, and entropy production as functions of the Hartmann number and inclination parameters. The Bayesian Regularization algorithm was employed to train the multilayer perceptron network model. Prediction results obtained from the model with 10 neurons in the hidden layer were compared with the target values and showed excellent agreement.The developed artificial neural network model successfully predicted the Nusselt number, Bejan number, and entropy productionwith average deviation rates of−0.007%,−0.11%, and 0.0002%, respectively.
Abstract
Minimizing entropy production is critically important, particularly in nanofluid flows. Applying this principle to flows with porous cavity structures helps optimize heat [...]
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Truncated Modified Weighted Exponential Distribution with Different Estimation Methods and Applications
A. Al-Omari, K. Aidi, G. Alomani

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 47

Abstract
This study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with an additional parameter and truncating its support to a finite range, enhancing its adaptability for modeling lifetime and reliability data. The statistical properties of the TrMWE, including moments, the moment-generating function, the quantile function, and order statistics, are examined. Parameter estimation is performed via maximum likelihood estimation (MLE), least squares and weighted least squares methods, maximum product of spacing method, Cramer-Von-Mises method, Anderson-Darling method, and right and left tails Anderson-Darling methods, with analysis of the asymptotic behavior of the estimators. Rényi and Tsallis entropies are also derived to assess the distribution’s uncertainty. The practicality of the TrMWE is illustrated using three real datasets and compared with existing distributions based on goodness-of-fit criteria, such as the Akaike Information Criterion and Bayesian Information Criterion. The results highlight the distribution’s flexibility and superior performance in modeling complex datasets.OPEN ACCESS Received: 14/08/2025 Accepted: 05/11/2025 Published: 03/02/2026
Abstract
This study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with [...]
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Theoretical Analysis of Seepage Field Distribution Induced by Pre-Excavation Dewatering Considering the Thickness of Waterproof Curtain and Delayed Responses of Water Table
D. Meng, J. Han, W. Lei, H. Zhang, C. Guan

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 46

Abstract
Dewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten structural stability. To mitigate such risks, suspended waterproof curtains have been widely employed to elongate seepage paths and reduce groundwater flow velocities. However, accurately predicting seepage field, especially under transient groundwater conditions with phreatic surfaces and varying curtain geometries, remains challenging. This study develops a theoretical model addressing transient groundwater seepage in foundation pits, explicitly considering a moving phreatic surface, curtain penetration depth and thickness. The proposed analytical solution is validated against experimental results and numerical simulations performed using COMSOL Multiphysics. Parametric analyses reveal that decreasing the vertical distance between the retaining wall base and the impermeable layer from 30 to 10 m reduces external groundwater drawdown by approximately 52%. Additionally, thicker waterproof curtains markedly decrease internal drawdown magnitudes, redirect seepage pathways, and effectively lower external groundwater depletion. Analyses on specific yield reveal delayed water release significantly moderates drawdown rates, reducing ultimate drawdown magnitudes. Furthermore, elevated internal excavation water levels intensify hydraulic head differences, substantially extending seepage-affected zones and amplifying drawdown responses both inside and outside the foundation pit. Overall, these findings provide critical theoretical insights for optimizing foundation pit design and improving dewatering practices, ensuring excavation safety and mitigating environmental impacts.OPEN ACCESS Received: 11/08/2025 Accepted: 05/11/2025 Accepted: 03/02/2026
Abstract
Dewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten [...]
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NAS-Driven Quantitative Assessment of Overpressure Genesis in Sedimentary Basins
H. Chen, C. Li, Y. Xu, S. Deng, H. Zhang, Z. Hu, H. Pan

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 45

Abstract
The study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation and distribution patterns of hydrocarbon resources is essential for practical hydrocarbon exploration. However, the prediction of anomalous high-pressure genesis currently encounters numerous challenges, including the complexity of overpressure genesis, the superimposed effect of multiple mechanisms, and the dependence of traditional models on manual analysis, resulting in inefficiency and quantification challenges. To this end, this paper proposes a method for identifying and quantitatively evaluating stratigraphic overpressure mechanisms based on neural architecture search, to enable rapid and accurate quantitative evaluation of overpressure mechanisms. The results show that the main anomalous high-pressure genesis mechanisms in the target work area include undercompaction, fluid expansion and tectonic compression, with contribution rates of approximately 73% for undercompaction, and 9% and 18% for fluid expansion and tectonic compression, respectively. The model’s accuracy in the test set reaches 95.4%, significantly enhancing the identification accuracy of anomalous stratigraphic pressure genesis and its superposition relationships. The innovation of this paper lies in the combination of wave velocity-density rendezvous map with clustering algorithm and neural architecture search algorithm, offering an efficient approach to identify multiple overpressure genesis mechanisms and predict pore pressure through machine learning algorithms, which is of great theoretical significance and practical application value.OPEN ACCESS Received: 08/08/2025 Accepted: 14/10/2025 Published: 03/02/2026
Abstract
The study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation [...]
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Heat Transfer and Electroosmotic Flow over Stretching Sheet: A Sensitive Analysis through Response Surface Method
J. Prakash, D. Tripathi, O. Bég, S. Kuharat, K. Mekheimer

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 44

Abstract
Recent advancements in electro-osmotic surface coatings have led to significant theoretical and numerical exploration of how zeta potential influences the electroosmotic flow of viscous ionic fluids over a stretching sheet. The governing boundary layer equations are derived from the fundamental laws of mass, momentum, and energy conservation using appropriate similarity transformations and non-dimensionalization techniques. This system of equations is solved numerically using MATLAB’s bvp4c solver. The accuracy of the computational results is confirmed through comparison with previously published studies. To better understand the influence of various parameters on flow and thermal behavior, Response Surface Methodology and Factorial Plot analysis are applied. These statistical tools enable sensitivity analysis by systematically investigating the effects of zeta potential, electroosmosis parameter, electric field strength, and Prandtl number on key flow characteristics such as velocity, temperature distribution, skin friction coefficient, and Nusselt number. The results reveal that the electric field parameter plays a dominant role in enhancing axial velocity and increasing skin friction, making it a key factor in flow dynamics. The zeta potential significantly influences the boundary layer by modifying the electrical double layer and surface charge distribution, leading to noticeable deceleration. Meanwhile, the Prandtl number primarily governs thermal gradients and heat transfer rates, controlling the thermal behavior of the fluid. These physical insights, combined with the optimization capability of Response Surface Methodology, provide actionable guidelines for the design of electroosmotic coating processes and lab-on-chip biomedical devices.OPEN ACCESS Received: 07/08/2025 Accepted: 17/10/2025 Published: 03/02/2026
Abstract
Recent advancements in electro-osmotic surface coatings have led to significant theoretical and numerical exploration of how zeta potential influences the electroosmotic flow [...]
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Investigation on Dynamical Mechanics of Rock-Backfill Composite Samples under SHPB Test
H. Ma, Y. Zhang, X. Feng

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 43

Abstract
In blast-induced caving mining with backfilling, understanding the interaction mechanisms and deformation evolution between rock and cemented tailing backfill (CTB) under coupled conditions is essential for ensuring stability. This study conducted dynamic uniaxial impact tests using the Split Hopkinson Pressure Bar (SHPB) system on rock-CTB composite specimens to investigate their mechanical response at high strain rates. Stress–strain relationships were recorded across a range of strain rates, and corresponding failure mechanisms were analyzed. A coupled SHPB model was also developed using GDEM software to simulate internal stress wave propagation and crack evolution within the composite specimens. Experimental results revealed that the dynamic compressive strength initially increases, then decreases, and eventually stabilizes as the average strain rate (ASR) increases from 27.45 s−1to 68.73 s−1. At strain rates below 60 s−1, the stress–strain curves exhibit a “stress drop” pattern, whereas above 60 s−1, a “stress rebound” behavior is observed. Energy absorption increases with ASR up to 55 s−1, then decreases, followed by a secondary increase. Numerical simulations validated the experimental findings, revealing the formation of both transverse and longitudinal cracks within the CTB. Greater deformation was observed near the transmission bar interface compared to the rock interface. These results offer valuable insights into the dynamic failure behavior of backfilled systems and inform improved backfill design in blast-induced mining operations.OPEN ACCESS Received: 05/08/2025 Accepted: 05/09/2025 Published: 03/02/2026
Abstract
In blast-induced caving mining with backfilling, understanding the interaction mechanisms and deformation evolution between rock and cemented tailing backfill (CTB) under [...]
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AI-Driven Multimodal Analysis of User Experience in Immersive Environments: A Case Study of The Sphere
Y. Seo, H. Ryoo

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 42

Abstract
Artificial intelligence (AI) and sensing technologies are reshaping how people experience immersive environments. This study investigates how audiences perceive and emotionally respond to such environments through an AI-driven mixed-methods analysis. A dataset of 275 usergenerated YouTube videos documenting experiences with The Sphere, an AI-convergent immersive environment, totaling over 3000 min of content and 24 million cumulative views, was analyzed to extract experiential themes, dominant emotions, and their relationships with public engagement metrics. The analysis identified seven key experiential themes: Awe of the Display, Personalized Spatial Audio Experience, Full-Body Sensory Engagement, Dynamic Visual Spectacle, Joyful Human–AI Encounter, Futuristic Spatial Design Experience, and Transformative Event Environment. Sentiment analysis revealed that fear was the most dominant emotion in textual narratives (42.3%), followed by surprise, sadness, happiness, and anger, whereas video-based analysis highlighted happiness (25.8%) and sadness (24.5%) as the most salient visual emotions. This contrast suggests that linguistic expressions emphasized feelings of awe and overwhelm, while visual cues reflected affective immersion and emotional depth. Regression results showed that Awe of the Display had the strongest positive impact on engagement (views, likes, comments), while Personalized Spatial Audio Experience showed a negative effect. These findings deepen the understanding of user experience in immersive environments and demonstrate how AI-assisted multimodal analysis can reveal the dynamics between audience perception and engagement in next-generation immersive environments.OPEN ACCESS Received: 12/11/2025 Accepted: 15/12/2025 Published: 03/02/2026
Abstract
Artificial intelligence (AI) and sensing technologies are reshaping how people experience immersive environments. This study investigates how audiences perceive and emotionally [...]
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AI-Driven Predictive Analytics for Demand Forecasting in Transportation Logistics to Enhance Supply Chain Agility
K. Abuhasel

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (2), 41

Abstract
The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and efficiency of the entire supply chain. Most of the time, traditional forecasting models are not entirely accurate in response to life-changing factors like weather, traffic, and inventory levels. The present research intends to build an AI-powered predictive model that can seamlessly enhance not only demand forecasting and logistics but also by the integration of real-time data. The framework incorporates several Machine Learning (ML) models, which are Light GBM for demand forecasting, Random Forest for disruption prediction, Linear Regression for shipping cost estimation, and Support Vector Regression for delivery time deviation prediction. A thorough dataset containing historical demand, weather conditions, traffic, and stock levels was used for the model’s training and evaluation, and its performance was monitored using MAE, MSE, RMSE, and MAPE metrics. The findings indicate that the suggested framework is a lot better than the existing ones, with Light GBM getting the lowest MAE (0.056), MSE (0.005), RMSE (0.072), and MAPE (0.142). This means that the new system can predict much better than before, thus making it possible for the company to take the right decision at the right time and consequently improving the overall supply chain efficiency. The research paper reveals the future possibilities of AI-based solutions for optimising logistics operations and building supply chain resilience.OPEN ACCESS Received: 20/10/2025 Accepted: 25/12/2025 Published: 03/02/2026
Abstract
The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and [...]
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Parametric Study of the Mechanical Behaviour of Membrane Tensegrity
L. Ferragu, J. Rebeck, R. Oval

membranes2025.
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Integration of parametric design and analysis tools
P. Kast

membranes2025.
- 4
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Adaptive Parametric Modelling via Grasshopper for Simulation of the Erection Sequence in Large-Span Cable Roofs: Correlation with Data Recorded during Construction
A. Borjabaz, I. Hernández, J. Llarena

membranes2025.
- 4
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The Effect of Slits and Notches in Fabric Specimens under Biaxial Tension
M. Boljen, F. Kölble, W. Harwick, R. Taylor

membranes2025.
- 22
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Evaluation of Equivalent Membrane Stiffnesses of Single-Set Rope Meshes
D. Drayer

membranes2025.
- 8
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Development of a new ring model for flexible protection networks
J. Sigeneger, V. Singer, R. Wüchner

membranes2025.
- 6
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An Optimization-based Approach to Shell Morphing
L. Mariani, V. Varano, S. Gabriele, L. Greco, M. Cuomo

membranes2025.
- 4
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Adjoint-based Stress Identification via Material Tensor Reconstruction in Membrane Structures using Displacement or Strain Measurements
T. Ansari, S. Warnakulasuriya, I. Antonau, A. Goldbach, R. Löhner, H. Antil, F. Airaudo, R. Wuechner

membranes2025.
- 18
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Testing of welded ETFE-foils for quality assurance
D. Runge, J. Uhlemann, N. Stranghöner

membranes2025.
- 4
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Long-term behaviour of ETFE films - Evaluation of monoaxial creep and relaxation tests after 100,000 hours of loading
K. Moritz, G. Müller, F. Goecke

membranes2025.
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- 5/5
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Failure Process of ETFE Foil
D. Karádi, D. Hegyi

membranes2025.
- 6
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Application of nonlinear constitutive and yield models in the design of ETFE cladding elements
A. Comitti, L. Seixas, A. Cabello, A. Bown, C. Paech, F. Bosi

membranes2025.
- 26
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The impact of textile shading systems in façade retrofitting: evaluating the operational phase in the LCA
G. Procaccini, C. Monticelli, A. Angelotti

membranes2025.
- 10
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Post-Consumer Recycled (PCR) ETFE Foil: Recycling of the Chelsea and Westminster Hospital ETFE Roof
K. Solanki, C. Monticelli, M. Marcos

membranes2025.
- 33
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Picture This: A New Perspective on Tensile Membrane Degradation
P. Romain, R. Ball

membranes2025.
- 5
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Stabilizing cable domes
J. Llorens

membranes2025.
- 2
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Multi Purpose Membrane Structure in Abano Terme
B. Stimpfle

membranes2025.
- 4
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Integrated Detailing and Thermal Optimization in a Tent-Based Textile Structure
T. Thommen

membranes2025.
- 124
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Fundamental Design Principles for Tensile Membrane Structures: A Call for Structural Awareness in Architectural Practice
C. Pérez

membranes2025.
- 2
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A Second Life for PVC/PES Membrane Material: Re-Use-Membrane Roof for a Modular Timber Structure Pavillon
J. Cremers, Y. Zelenka, S. Baier

membranes2025.
- 5
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The Avicii Arena retractable ceiling - Innovative design and installation
T. Santos, A. Cernysevas

membranes2025.
- 6
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Development of a pneumatic actuator based on bio-PU coated fabrics for architectural applications
R. Aditra, S. Hidalgo, A. Comitti, P. Beccarelli, D. Misseroni, F. Bosi

membranes2025.
- 60
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A Deep Learning Network based on Channel and Temporal Attentions for Decoding Motor Imagery EEG Signals
S. Zou, Y. Shi, Q. Wei, D. Jiang, J. Wu

Jianhua Wu's personal collection (2026). 1

Abstract
(1) Background: Accurately decoding motor imagery (MI) tasks is a prerequisite for creating a MI-based brain-computer interface (BCI). However, low signal-to-noise ratio and non-stationarity of EEG signals present a huge challenge for the classification of MI-EEG signals, restricting the extensive development of the BCI industry.''' '''(2) Methods: In this paper, we propose a novel deep learning model CTANet that integrates both channel and temporal attention mechanisms into a convolutional neural network to improve the classification accuracy of the MI-BCI systems. The model is constituted first by three serially connected temporal, spatial, and temporal convolution layers to extract features from the brain signals, with an efficient channel attention module inserted between the second and the third convolutional layers to highlight useful feature channels. Subsequently, the time segment for task decoding is partitioned into several time windows, and a variance layer is employed for computing the logarithmic variance of each window. Next, a multi-head attention mechanism is adopted to extract temporal dependency of features from different windows. Finally, a fully connected layer is used for classifying MI-EEG signals. (3) Results: The performance of the proposed model was evaluated on two publicly available BCI datasets and compared with the state-of-the-art methods. The experimental results show that for dataset BCIC-IV2a, our network achieved classification accuracies of 81.17% and 84.33% in inter-session and intra-session scenarios respectively, whereas for dataset OpenBMI, our network achieved classification accuracy of 73.06% and 77.59% in inter-session and intra-session scenarios respectively. (4) Conclusions: These results outperform state-of-the-art networks, indicating significant potential of the proposed model CTANet in MI decoding.
Abstract
(1) Background: Accurately decoding motor imagery (MI) tasks is a prerequisite for creating a MI-based brain-computer interface (BCI). However, low signal-to-noise ratio and [...]
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A data-driven model of waste gasi pyrolysis: One tailored approach for an experimental facility from the Czech Republic
D. Brkić, P. Praks, J. Nagyová, M. Běloch, M. Marek, J. Najser, R. Praksová, J. Kielar

Science Progress (2026). Brkić D, Praks P, Buchlovská Nagyová J, et al. A data-driven model of waste gasification and pyrolysis: One tailored approach for an experimental facility from the Czech Republic. Science Progress. 2026;109(1). doi:10.1177/00368504251412556

Abstract
The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents aﬂexible, portable, and expandable waste-to-energy concept that integrates gasiﬁcation and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modelling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB– Technical University of Ostrava, Czech Republic: (i) A primary gasiﬁcation facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modelled and simulated using in-house software developed in Python, employing
Abstract
The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering [...]
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Advanced Computational Study of Nonlinear Time-Fractional Newell-Whitehead-Segel Equation with Caputo-Fabrizio Derivative Using B-Spline Techniques
M. Shafique, M. Yaseeen, S. Trabelsi, M. Balti

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 42

Abstract
This study presents numerical solutions for the time-fractional NewellWhitehead-Segel (NWS) equation with a Caputo-Fabrizio derivative. Spatial derivatives are discretized using three B-splines-cubic, cubic trigonometric and extended cubic B-splines-while temporal discretization is handled by a finite difference scheme. The proposed schemes are rigorously analyzed for stability and convergence. Their performance is evaluated in terms of accuracy and computational efficiency. Numerical experiments confirm the effectiveness of these techniques in capturing the dynamics of the fractional NWS equation. Each B-spline variant demonstrates unique strengths, highlighting the flexibility of B-spline approaches for solving fractional differential equations with nonlocal, memory-dependent operators. These results affirm the reliability and robustness of B-spline-based methods for such problems, paving the way for future advancements in this area.OPEN ACCESS Received: 23/08/2025 Accepted: 08/12/2025
Abstract
This study presents numerical solutions for the time-fractional NewellWhitehead-Segel (NWS) equation with a Caputo-Fabrizio derivative. Spatial derivatives are discretized [...]
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Adaptive Power Splitting Strategies for Smart Microgrids with Enhancing Energy Efficiency and Resilience through Dynamic Load Management
A. Shaher, Z. Mushtaq, G. Abbas, H. Alwadie, M. Irfan, H. Murtaza, A. Shaheen, S. dawsari

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 41

Abstract
The integration of a renewable energy distributed generation into microgrids poses a significant constraint in the way power is managed, further so due to the natural variability in renewable generation and the variability in the load demands. To address these issues, this paper introduces a novel approach to the Spider Swarm Optimization (SSO) algorithm, the Dynamic LoadAdaptive Power Splitting (DLAPS) strategy, to enable real-time adaptive power sharing and enhance system resilience. Unlike the classical methods of power allocation that are static, according to which the power is divided between sources of renewable energy and storage systems, and between these sources and critical loads, the DLAPS-SSO applies the idea of a machine learning based predictive model to predict the power and dynamically optimize power allocation between the sources of renewable energy and storage systems and the sources and the critical loads. The model provides a multi-objective optimization framework that aims to minimize power losses and grid frequency variations, and to maximize the system’s resilience to disturbances, including disconnection from the grid, component malfunctions, and the availability of renewable energy sources. The comparison of simulation results with those of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods shows that the energy efficiency of the DLAPS-SSO increases by 15%–20%, and the amount of power losses across various load profiles decreases by 30%–35%. Moreover, the proposed solution offers 60% faster recovery time in case of grid disconnection, maintains 65.9% of the critical load in case of component failure, and provides 40%–50% less resilience than state-of-the-art techniques. The analysis of seasons and real data shows that there is stability of the behavior with the increase of efficiency (18%–22% during winter, and 23%–25% during summer), and the ability of the suggested approach to be robust when changing plant configuration/operation. Integration of optimization of dynamic load management and adaptive power splitting will spur microgrid control strategies and offer a viable strategy to stabilize the grid, reduce operation costs, and enable sustainable changes in energy transformations. The results demonstrate the essential role of bio-inspired optimization and reactivity in the next generation of smart grids.
Abstract
The integration of a renewable energy distributed generation into microgrids poses a significant constraint in the way power is managed, further so due to the natural variability [...]
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An Integrated IVHFS and DEMATEL-ANP Framework for Competitive Intelligence Evaluation in Smart Factories
W. Lo, M. Li, D. Huang, C. Yang, W. Deng, S. Li

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 40

Abstract
In the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making and maintain technological and operational advantages. However, the highly dynamic and complex nature of the smart manufacturing environment introduces considerable uncertainty, hesitation, and interdependencies among evaluation indicators, posing significant challenges to traditional decision-making frameworks. To address these issues, this study proposes an integrated framework that combines interval-valued hesitant fuzzy sets (IVHFS) with the decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP). IVHFS is employed to capture the ambiguity and hesitation inherent in expert judgments, enabling a more flexible and realistic representation of evaluation inputs. Subsequently, the DEMATEL-ANP approach is used to uncover the causal relationships among CI indicators and to construct a network-based weighting structure that reflects their interdependencies. A case study in a smart factory is conducted to validate the practicality and effectiveness of the proposed framework, and a sensitivity analysis confirmed its stability.OPEN ACCESS Received: 04/08/2025 Accepted: 28/10/2025 Published: 23/01/2026
Abstract
In the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making [...]
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Biological Solitons in Biomembranes: Analytical Solutions of a Boussinesq-Type Equation with Amplitude-Dependent Nonlinearity
F. Abbas, A. Raza, F. Zaman, S. Baloch

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 38

Abstract
Boussinesq-type equations (BTE) emerge in various fields of fluid and solid mechanics, particularly where nonlinearities and dispersion are considered. Boussinesq-type equations are used to model wave effects in biomembranes, particularly longitudinal waves. They can account for nonlinear and dispersive effects that are important for characteristic wave behavior in biomembranes, composed of lipids, with distinct nonlinear effects. This provides a realistic description of longitudinal mechanical waves in nerve membranes. In this research, we investigate the Boussinesq-type equation that describes the waves in biomembranes with amplitude-dependent nonlinearities, using the Khater method (KM) and the Jacobi elliptic function method (JEFM). In addition to producing generic biological answers, the proposed methods allow the analysis of single wave solutions. These methods make it easier to derive solutions for solitary waves, which occur in a variety of forms, including bell, antibell, periodic, anti-kink and kink solitons. Each of these waves has a wide range of possible applications in biomathematics. Some of the findings are displayed as contour, 2D, and 3D graphics with particular parameter values applied under the specified conditions in order to highlight the important propagation properties. To the best of our knowledge, the biological solitons of the considered model have not been reported by using the proposed techniques in the literature. These results provide new theoretical insights into wave phenomena in biomembranes and may contribute to biological physics and nonlinear science.OPEN ACCESS Received: 04/11/2025 Accepted: 15/12/2025 Published: 23/01/2026
Abstract
Boussinesq-type equations (BTE) emerge in various fields of fluid and solid mechanics, particularly where nonlinearities and dispersion are considered. Boussinesq-type equations [...]
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Fractional-Order Resilient Control for UAV–USV Cooperation under Actuator Constraints, Signal Attacks, and Wind Gusts
Y. Qiao, S. Asghar, M. Arab, M. Awadalla, S. Trabelsi, A. Niazi

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 37

Abstract
The paper presents a resilient dynamic adaptive event-triggered sliding mode control (DAET–SMC) framework for fractional-order delayed multi-agent systems under actuator saturation, stochastic disturbances, and cyber-attacks. Existing methods often fail to ensure containment and formation stability when multiple practical constraints coexist. The proposed approach leverages Riemann–Liouville fractional dynamics to capture system memory effects and integrates adaptive compensation to mitigate actuator faults, measurement attacks, and communication delays. Numerical simulations on a 16-agent network with one leader and fifteen followers show that all followers achieve containment within 20 s, with formation errors below 10−2m, while maintaining bounded control effort. Compared with conventional non-adaptive controllers, the proposed method demonstrates faster convergence, superior robustness, and resilience under combined disturbances, achieving up to 35% faster error convergence and maintaining control input within saturation limits. These results confirm the effectiveness of the DAET–SMC strategy for practical multi-agent coordination in uncertain and constrained environments.OPEN ACCESS Received: 30/10/2025 Accepted: 26/11/2025 Published: 23/01/2026
Abstract
The paper presents a resilient dynamic adaptive event-triggered sliding mode control (DAET–SMC) framework for fractional-order delayed multi-agent systems under actuator [...]
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Robust Neutrosophic Ratio-Type Estimators Using REWLSE: A Simulation-Based Approach for Efficient Mean Estimation under Outlier-Contaminated Data
K. Abuhasel

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 36

Abstract
The exact estimations of population mean under the influence of indeterminacy and data contamination are a long-standing issue in survey sampling. Traditional ratio-type estimators are highly sensitive to influential observations, and the neutrosophic methods that are currently used do not effectively describe robustness in the face of uncertainty. The current research constructs a generalized family of neutrosophic robust ratio-type estimators that are developed in the context of Robust and Efficient Weighted Least Square Estimation (REWLSE) framework. Bias and mean square error (MSE) expressions are analytically derived for Ordinary Least Squares (OLS) and REWLSE frameworks in order to allow extensive comparisons between theory and efficiency. Monte Carlo simulations on neutrosophic data are systematically used to study the finitesample behavior of proposed estimators, and an empirical evaluation of these estimators is done using actual temperature data. The simulation and empirical evidence have repeatedly shown that suggested REWLSEbased neutrosophic estimators have significant efficiencies, they remain highly resistant to outliers, and perform better than OLS-based ones. These results support the effectiveness of the suggested framework and highlight its potential to become a powerful and trustworthy alternative to population mean estimation in uncertain, imprecise, and contaminated data environments.OPEN ACCESS Received: 13/10/2025 Accepted: 12/11/2025 Published: 23/01/2026
Abstract
The exact estimations of population mean under the influence of indeterminacy and data contamination are a long-standing issue in survey sampling. Traditional ratio-type estimators [...]
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Robust Finite Population Mean Estimation under Outlier Contamination Using Adaptive UK’s Redescending M-Estimation
K. Abuhasel

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 35

Abstract
The efficient estimation of population parameters under non-ideal data conditions remains a critical challenge in survey sampling. Traditional estimators based on ordinary least squares (OLS) often yield unreliable results when datasets contain outliers or deviate from normality. This study introduces a new class of ratio-type estimators that incorporate population parameters such as the median and decile mean and are developed under both OLS and UK’s redescending M-estimation frameworks. To further enhance robustness, an adaptive variant of the UK’s redescending M-estimator is proposed, which automatically adjusts its tuning constant based on the degree of contamination. Analytical derivations of bias and mean square error (MSE) confirm the superiority of the proposed estimators over their OLS counterparts. Empirical validation using realworld socio-economic survey data and extensive simulation studies across varying sample sizes, outlier rates, and distributional forms demonstrate that the adaptive UK’s redescending estimator achieves substantial efficiency gains and reduced bias, even under high contamination levels. The results establish the adaptive redescending M-estimation approach as a robust and computationally efficient alternative for finite population mean estimation in the presence of outliers.OPEN ACCESS Received: 10/10/2025 Accepted: 03/11/2025 Published: 23/01/2026
Abstract
The efficient estimation of population parameters under non-ideal data conditions remains a critical challenge in survey sampling. Traditional estimators based on ordinary [...]
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Enhancing Wind Turbine Reliability: A Hybrid State-Space and Generative Approach to SCADA-Based Fault Detection
A. Shaher, N. Khan, Z. Mushtaq, A. Hassan, M. Irfan, H. Alwadie, S. Dawsari, S. Rahman

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 34

Abstract
Wind turbine reliability is essential for the renewable energy sector, as failures in key parts such as gearboxes and main bearings lead to more than $10 billion in downtime and maintenance costs each year. Supervisory control and data acquisition (SCADA) systems can monitor turbines using signals such as vibration, power output, and wind speed; however, applying machine learning to this data type is challenging due to the presence of unbalanced fault types and complex time patterns. Previous research has explored physics-informed deep learning, digital twins, and contrastive learning, achieving noteable fault detection accuracy. However, challenges remain in detecting rare faults, dealing with imbalanced data, combining data sources, and model generalization. This study presents StateSpaceNetWithGen (SS-Gen), a hybrid model integrating state-space modeling for temporal dynamics with generative augmentation for class imbalance. Tested on a 35,000-sample SCADA dataset (2018–2019), SS-Gen achieved high accuracy (≈1.00) and F1-score (≈1.00) on this specific dataset, improving by 33% over baselines. To further validate the strengths of the proposed method, the methodology is validated on a second dataset with different distribution. These results support more reliable and interpretable wind turbine health monitoring and move the field toward stronger physics-informed and federated machine learning solutions.OPEN ACCESS Received: 06/10/2025 Accepted: 19/11/2025 Published: 23/01/2026
Abstract
Wind turbine reliability is essential for the renewable energy sector, as failures in key parts such as gearboxes and main bearings lead to more than $10 billion in downtime [...]
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Adaptive Federated Fault Diagnosis Framework for Wind Turbine Reliability
M. Irfan, N. Khan, M. Abubakr, Z. Mushtaq, H. Alwadie, A. Shaher, S. dawsari, S. Rahman

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 31

Abstract
Wind turbine reliability is critical for sustainable energy production, yet fault diagnosis faces challenges due to data privacy concerns, heterogeneous operational conditions, and resource constraints in distributed wind farms. Traditional centralized Machine Learning (ML) approaches struggle with these issues, necessitating decentralized solutions. This study introduces the Adaptive Federated Fault Diagnosis (AF2D) framework, a novel Federated Learning (FL) approach for wind turbine fault diagnosis that ensures data privacy while addressing non-i.i.d. data distributions. Using a dataset of 35 uniaxial vibration recordings from six turbines at the University of Mustansiriyah, AF2D leverages two key modules: Adaptive Model Aggregation (AMA) and Lightweight Model Optimization (LMO). AMA employs Jensen-Shannon divergence and cosine similarity to adaptively aggregate local model updates, mitigating data heterogeneity, while LMO applies structured pruning (60% filter reduction) and 8bit quantization to enable deployment on resource-constrained SCADA systems. Results show AF2D achieves 91.3% accuracy (±1.2%, 95% confidence interval), a 3.5% improvement over FedAvg (87.8%± 1.4%), with statistical significance (p < 0.05), and outperforms state-of-the-art methods like Clustered FL (88.5%) and Privacy-Preserving FL (87.2%). LMO reduces inference time by 64.44% and memory usage by 53.71%, enhancing edge deployment feasibility. However, the small dataset raises overfitting risks, and scalability tests reveal a threefold communication cost increase (54.5 to 150.6 MB) for 18 clients, mitigated by proposed compression (30%–50% reduction) and asynchronous updates (20%–40% overhead reduction). Privacy is maintained with a differential privacy guarantee of= 1.0, though advanced techniques like secure multiparty computation could achieve <1. Despite limitations in severe fault detection and dataset diversity, AF2D demonstrates robust performance. Future work includes integrating multi-modal data (SCADA, vibration, environmental), testing real-time deployment, and expanding federated datasets to enhance generalizability and scalability.OPEN ACCESS Received: 11/09/2025 Accepted: 16/10/2025 Published: 23/01/2026
Abstract
Wind turbine reliability is critical for sustainable energy production, yet fault diagnosis faces challenges due to data privacy concerns, heterogeneous operational conditions, [...]
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A Novel Sin Model SMEx with Application on COVID-19 and Precipitation Data
A. Alrumayh, H. Khogeer

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 33

Abstract
The present study proposes a new and flexible trigonometric extension of the moment exponential distribution, termed the Sine Moment Exponential (SMEx) distribution, developed using the sine-G family of distributions. This model offers an attractive alternative to well-known lifetime distributions by providing enhanced flexibility for analyzing lifetime datasets that exhibit leptokurtic or platykurtic behavior. Several statistical properties of the SMEx distribution are derived, including its moments, quantile function, mean residual life, and order statistics. To assess its performance, five different estimation approaches are applied, including Anderson-Darling estimation, maximum likelihood estimation, Cramervon Mises estimation, ordinary least squares estimation, and weighted least squares estimation. A detailed Monte Carlo simulation study is utilized to illustrate the estimation behavior of these considered estimation procedures. In the end, two datasets associated with COVID-19 and precipitation are utilized to illustrate the applicability and flexibility of the proposed distribution. It is found that the proposed distribution efficiently analyzed these datasets as compared to competitive distributions.OPEN ACCESS Received: 06/10/2025 Accepted: 28/10/2025 Published: 23/01/2026
Abstract
The present study proposes a new and flexible trigonometric extension of the moment exponential distribution, termed the Sine Moment Exponential (SMEx) distribution, developed [...]
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Enhancing Wind Power Forecasting Using Hybrid Multi-Head Attention and 1-Dimensional Convolutional Neural Networks
S. Rahman, A. Shaher, N. Khan, M. Abubakar, Z. Mushtaq, H. Alwadie, A. Hindi, M. Irfan, S. dawsari

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 32

Abstract
The accurate forecasting of wind power plays a veritable part in integrating renewable energy from wind turbines into power grids. Wind power, being a highly volatile mode of energy generation owing to temporal variations and complex weather patterns, renders reliable predictions essential for energy management and grid stability. In order to tackle this, we propose a hybrid Multi-Head Attention and 1D-Convolutional Neural Network (MHA-CNN) architecture that combines attention mechanisms and convolutional layers to capture both long-term dependencies and localized features in time-series data from a Supervisory Control and Data Acquisition (SCADA) system. The model effectively improves forecasting performance by attaining an R2score of 99.42 for hour-ahead and 96.52 for day-ahead predictions on a 50,540-sample, 10-min SCADA dataset using 5-fold chronological cross-validation, outperforming traditional methods without any manual feature engineering. The proposed method is also evaluated across multiple scenarios to assess the robustness of the proposed approach.OPEN ACCESS Received: 01/10/2025 Accepted: 10/11/2025 Published: 23/01/2026
Abstract
The accurate forecasting of wind power plays a veritable part in integrating renewable energy from wind turbines into power grids. Wind power, being a highly volatile mode [...]
- 44
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Simulation of Hybrid and Porous Spur Gears with FEA
M. Bähr, O. Koch, M. Oehler

SIM-AM2025.
- 4
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Analytical and Numerical Investigation of Sandwich Beams with Additively Manufactured Lattice Cores and Composite Facesheets
E. Dereli, C. Mittelstedt

SIM-AM2025.
- 4
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Analytical Investigation of Global and Local Instability in Additively Manufactured Lattice-Core Sandwich Columns
S. Osmanoglu, C. Mittelstedt

SIM-AM2025.
- 6
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A Thermal-History-Informed Inherent Strain Framework for Efficient Distortion Prediction in PBF-LB
I. Setien, M. Chiumenti, M. Sebastian

SIM-AM2025.
- 8
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From Computed Tomography to Finite Element Analysis: Mapping Porosity in Metal Additive Manufacturing
L. Cacho, M. Neto, D. Neto, M. Vieira

SIM-AM2025.
- 14
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Variations in morphology and microstructure caused by different melt pool dynamics in laser powder bed fusion of ultrathin walls
S. Belousov, M. Bogdanova, A. Perepelkina, I. Iskandarova, B. Korneev, A. Zakirov, B. Potapkin

SIM-AM2025.
- 40
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Analytical LPBF Melt Pool Simulation and Experimentation for Metamaterial Lattices
W. Wits, C. Vos, T. Koenis, M. De Smit

SIM-AM2025.
- 8
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Research on the Mechanical Characteristics and Structural Optimization of HighPressure Diaphragm Compressors in Hydrogen Refueling Stations under Service Conditions
M. Li, L. An, W. Hu, Y. Hou, Z. Wang

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 23

Abstract
To enhance the fatigue life and service safety of the diaphragm in high-pressure diaphragm compressors, this study investigated the realworld operating conditions of hydrogen refueling station diaphragm compressors. A refined finite element model of the gas cavity cover plate–diaphragm–oil cavity support plate assembly was established using Abaqus software. Static structural analysis, thermo-structural coupling analysis, and modal analysis were conducted to examine the stress distribution of the diaphragm assembly under extreme working conditions, the influence of bolt preload on the modal characteristics of the compressor, and the effect of diaphragm thickness on stress distribution and fatigue life. The research results indicate that air holes/passages and oil holes/passages significantly affect the stress distribution of the diaphragm. The high-stress areas of the diaphragm are mainly concentrated in the transition zone of the chamber and the overlapping area between the diaphragm and the air/oil passages. The temperature inside the diaphragm compressor’s membrane chamber significantly affects the stress level of the diaphragm. When the chamber temperature reaches 245°C, the maximum equivalent stress of the diaphragm reaches 1079 MPa. As the preload increases, the modal frequencies generally rise, with higher-order modes showing greater sensitivity to preload variations. Considering the stress level, fatigue life, and deflection performance of each diaphragm, the diaphragm thickness should be designed to be 0.4 mm. The finite element simulation model and research results proposed in this paper can provide a reference for the design improvement and selection of cavity types and diaphragms of diaphragm compressors in hydrogen refueling stations, as well as for the online health monitoring of hydrogen refueling stations.OPEN ACCESS Received: 31/07/2025 Accepted: 09/09/2025 Published: 23/01/2026
Abstract
To enhance the fatigue life and service safety of the diaphragm in high-pressure diaphragm compressors, this study investigated the realworld operating conditions of hydrogen [...]
- 20
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Numerical Analysis of Gyroid Structure performances in heat sink environment
N. Belli, G. Gaudenzi, L. Giorleo

SIM-AM2025.
- 54
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Energy-based form-finding of column-like lattice elements
M. Bruggi, C. Guerini, G. Novati

SIM-AM2025.
- 6
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Automatised Optimisation of Material Joints for Additive Manufactured Multimaterial Components
C. Leupold, M. Petersen

SIM-AM2025.
- 6
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Automated workflow with simulation-driven topology optimization for the economic design of hybrid-manufactured tool components
M. Fischer, P. Kostorz, M. Binder, C. Seidel

SIM-AM2025.
- 186
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Topology Optimization with Limits on Joint Loads
J. Soika, J. Schenk, M. Zimmermann

SIM-AM2025.
- 16
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Enhancing Engine Mount Design through Topology Optimization for Additive Manufacturing
I. NTINTAKIS, G. Stavroulakis, E. Lougiakis, M. Petousis

SIM-AM2025.
- 68
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Analysis on Spatter Behaviour in Powder Bed Fusion Using Solid-gas Multiphase Flow Analysis
Y. Nakamura, R. Koike, Y. Kakinuma, Y. Hirono, T. Mori, K. Yamazaki

SIM-AM2025.
- 41
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Shape Optimization of Thin-Wall Geometries for Layer-Based Direct Deposition Additive Manufacturing
F. Schito, J. Meza Zeron, K. Dörfler, P. D'Acunto, M. Hojjat

SIM-AM2025.
- 48
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Innovative AM Design Strategies for Integrating Carbon Fibre Composites with Metallic Structures to Support Lightweight Manufacturing
A. Islam, E. Pei, J. Wang, M. Alvarez-Leal, J. Ureña, C. Kelly, D. Flatharta, T. Marinopoulos

SIM-AM2025.
- 6
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Multi-scale thermal modelling and variable scan parameter optimization framework for homogeneous and predictable PBF-LB aerospace components
T. Koenis, S. Boheemen, M. Montero-Sistiaga, M. De Smit

SIM-AM2025.
- 42
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Tuning Curvature in Quadratic Regression via Caputo Fractional Derivatives: Theory and Applications
Y. Tandogdu, S. Ilgaz, M. Awadalla, Y. Yameni

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 30

Abstract
Classical regression can only examine the relation between response and predictor variables based on integer order calculus theory. What happens when non integer order calculus is considered is a field where a vast spectrum of studies can be undertaken. The purpose of this study introduces a novel fractional-order quadratic regression model grounded in the Caputo derivative framework, addressing the limitation and the rigidity of classical polynomial regression in adapting to the intrinsic curvature of data. The core innovation is the use of the fractional order ν as a tunable parameter for curvature-sensitive optimization. Our main contributions are fourfold: First, we establish a fundamental theoretical pillar by proving that the second-order Caputo derivative preserves the curvature direction of quadratic functions, enabling a principled optimization framework. Second, we rigorously demonstrate the model’s robustness by proving the existence and uniqueness of solutions via Banach’s fixed point theorem and establishing stability bounds through a fractional Grönwall inequality. Third, we develop a practical methodology to identify an optimal fractional order ν that minimizes the error-to-explained-variation ratio (SSE/SSR). Finally, we validate the framework on four diverse real-world datasets from air quality, soil science, education, and meteorology. The proposed model consistently outperforms classical quadratic regression, achieving a reduction in the SSE/SSR ratio by up to 21% in specific cases. The proposed method yields more efficient models with either lower estimation error or higher correlation coefficients, positioning Caputo fractional quadratic regression as a powerful and theoretically sound alternative for modeling cases where quadratic regression is considered appropriate.OPEN ACCESS Received: 10/09/2025 Accepted: 05/11/2025 Published: 23/01/2026
Abstract
Classical regression can only examine the relation between response and predictor variables based on integer order calculus theory. What happens when non integer order calculus [...]
- 18
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Investigation of Compensated Foundation SettlementMechanism Based on FEM-DEM Coupling Method
X. Zhang, L. Lv, M. Xu, G. Mei

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 39

Abstract
The application of compensated foundations is common in engineering, but significant settlements often occur during the application process. A foundation pit model supported by a rigid retaining wall was established based on the Finite Element Method-Discrete Element Method (FEMDEM), and a cantilevered layer-by-layer excavation process was simulated. A rolling resistance linear model was used to simulate the sand, while a variable particle size method was adopted to establish the foundation model. It is shown that during the excavation process of the foundation pit, the stress of the sand at the pit bottom gradually decreases, and the displacement of the soil changes gradually fromsettlement to uplift as one moves frombehind the retaining wall to the bottomof the foundation pit. Moreover, theporosityof the sandat thepit bottomgradually increases.As a result, the strength of the uplifted sand at the pit bottomdecreases due to over-excavation during the foundation pit excavation stage. The uplifted sand is finally excavated during the site leveling stage, resulting in a further decrease in the strength of the sand at the pit bottom. Finally, an inverted arch bottom plate structure is proposed to mitigate the over-excavation settlement of the compensated foundation.
Abstract
The application of compensated foundations is common in engineering, but significant settlements often occur during the application process. A foundation pit model supported [...]
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A New Scalable Hybrid Model Approach of High-Dimensional Time Series Forecasting Applications
N. Yaqoob, F. Alghamdi, A. Aloufi, R. Jabeen, A. Zaka

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 29

Abstract
This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach begins with Prophet’s decomposition of a time series into its fundamental components trend, seasonality, and holiday effects, and then applies Lasso regression to the residuals to capture additional structural patterns that fail to capture by the base model. This layered methodology boosts predictive accuracy by enabling the model to learn both systematic and irregular variations within temporal data by incorporating Lasso’s feature selection capability, the framework efficiently handles highdimensional datasets, retaining only the most informative predictors. The outcome is a hybrid model which achieves an optimal balance among interpretability, scalability, sparsity, and forecasting precision. Validation on simulated high-feature datasets and real-world electricity consumption data demonstrates that the Lasso–Prophet hybrid consistently outperforms the Prophet and other baseline models.OPEN ACCESS Received: 31/08/2025 Accepted: 17/11/2025 Published: 23/01/2026
Abstract
This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach begins with Prophet’s [...]
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Qualitative Analysis of Nonlinear Systems Involving Hadamard-Type Fractional Derivatives with Nonlocal Boundary Conditions and Stability Properties
M. Manigandan, M. Awadalla, R. Shanthi, S. Trabelsi

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 27

Abstract
This paper establishes a comprehensive analysis of a coupled system of nonlinear Hadamard-type fractional differential equations subject to generalized nonlocal integral boundary conditions. The distinct logarithmic kernel of the Hadamard derivative makes this framework particularly suitable for modeling scale-invariant processes and ultraslow diffusion phenomena. The existence and uniqueness of solutions are rigorously investigated using fixed point theory: Banach’s contraction principle ensures uniqueness, while the Leray-Schauder nonlinear alternative guarantees existence under more general growth conditions. Furthermore, the system is proven to be Ulam-Hyers stable, ensuring that approximate solutions remain close to exact solutions, which is crucial for the robustness of the model in practical applications. The theoretical findings are effectively validated through two detailed numerical examples, demonstrating the applicability of the established results to different classes of nonlinearities.OPEN ACCESS Received: 22/08/2025 Accepted: 03/11/2025 Published: 23/01/2026
Abstract
This paper establishes a comprehensive analysis of a coupled system of nonlinear Hadamard-type fractional differential equations subject to generalized nonlocal integral boundary [...]
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Solving the Sine-Gordon Equation: A Novel Numerical Approach Using Cubic B-Splines and the Method of Lines
I. Ashraf, M. Yaseeen, S. Trabelsi, M. Balti

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 28

Abstract
This work explores a numerical approach to solving the sine-Gordon equation using the method of lines combined with cubic B-spline interpolation. The sine-Gordon equation, a nonlinear partial differential equation, arises in various fields of physics and engineering, describing phenomena such as solitons in non-linear optics and magnetic flux lines in superconductors. In our approach the method of lines is used to discretize the spatial derivatives, thereby converting the partial differential equation into a system of ordinary differential equations. These ordinary differential equations are then solved numerically using standard techniques, specifically the Runge-Kutta method of order 4. Cubic B-spline interpolation is employed to approximate the spatial derivative, ensuring efficient and precise computation of the solution. A comprehensive stability analysis reveals that our scheme requires the time step conditiont1.53 h for numerical stability. Theoretical convergence analysis demonstrates that the method achieves O(h2)spatial convergence and O( t4)temporal convergence, resulting in an overall error bound of O(h2+ t4 ). These theoretical predictions are strongly supported by numerical experiments, where empirical convergence rates closely match the theoretical values. To validate the numerical scheme, the results are compared with existing solutions. Our findings demonstrate the accuracy and computational efficiency of the proposed method, highlighting its potential as a valuable tool for studying the dynamics and behavior of systems governed by the sine-Gordon equation.OPEN ACCESS Received: 29/08/2025 Accepted: 05/11/2025 Published: 23/01/2026
Abstract
This work explores a numerical approach to solving the sine-Gordon equation using the method of lines combined with cubic B-spline interpolation. The sine-Gordon equation, [...]
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Combining Copula Theory and Machine Learning for Prediction of Ground Vibrations Induced by Tunnel Blasting
Y. Cao, R. Ma, L. Zhang, X. Du, W. Liu, Q. Gao

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 26

Abstract
To effectively predict and control the peak particle velocity (PPV) induced by tunnel blasting, this study investigates the Qinhuangdao Jiaoshan Tunnel as a case study. This study first proposes an Improved Particle Swarm Optimization (IPSO) algorithm through theoretical derivation. Building upon IPSO, a further enhanced algorithm, termed CIPSO, is developed by integrating a dependency model derived from Copula theory. The CIPSO algorithm is then employed to optimize a Support Vector Regression (SVR) model, establishing the final CIPSO-SVR prediction framework. Copula theory was employed to quantify the correlation between PPV and surface cumulative settlement (S). A regularization term incorporating Kullback-Leibler (KL) divergence was then embedded into the SVR objective function. The Hyperparameters of the CIPSO-SVR model were optimized using fixed-step rolling cross-validation. The model’s predictive performance was rigorously compared against CIPSO-optimized Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, as well as against SVR, CNN, and LSTM models optimized by the Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The results show that the CIPSO-SVR model achieves superior accuracy and robustness on the test set (R2= 0.9569) in predicting PPV compared to the alternative models. Crucially, the model effectively captures the inherent nonlinear relationships of complex engineering problems, even with small-sample data.OPEN ACCESS Received: 18/08/2025 Accepted: 17/10/2025 Published: 23/01/2026
Abstract
To effectively predict and control the peak particle velocity (PPV) induced by tunnel blasting, this study investigates the Qinhuangdao Jiaoshan Tunnel as a case study. This [...]
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Bearing Fault Diagnosis Based on AVMD and HPO-DBN
X. Sha, F. Qian

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 25

Abstract
To overcome difficulties such as non-stationary vibrations, highdimensional feature redundancy, and mode selection issues that may arise during signal decomposition in bearing fault diagnosis. We propose an adaptive method called Adaptive Variational Mode Decomposition (AVMD) for extracting time-frequency domain characteristics from the bearing vibration displacement signals to the maximum extent possible. Next, the ReliefF algorithm is employed to select desired features, and an autoencoder is used to reduce the selected features dimensionally. Furthermore, because the Hunter-Prey Optimisation (HPO) algorithm can balance multiple objectives during the search process by utilising the concepts of hunter and prey to generate a better solution set, incorporating this algorithm into the Deep Belief Network (DBN) establishes an HPO-DBN fault diagnosis model. Subsequently, we validate the proposed method using both public datasets and field compressor data. Moreover, we compare the results with those obtained from the Support Vector Machine (SVM). The findings indicate that this approach enhances the bearing fault identification rate, thus supporting predictive maintenance of bearings.OPEN ACCESS Received: 13/08/2025 Accepted: 16/10/2025 Published: 23/01/2026
Abstract
To overcome difficulties such as non-stationary vibrations, highdimensional feature redundancy, and mode selection issues that may arise during signal decomposition in bearing [...]
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Comparative Study of Fuzzy Logic, P&O, Incremental Conductance, and Artificial Neural Network MPPT Methods in Fluctuating Irradiance
K. BOUJAGHAMA, N. Khaldi, R. Markazi, L. Amhaimar

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 24

Abstract
Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extraction under dynamic irradiance and temperature conditions, improving the efficiency of PV systems requires advanced Maximum Power Point Tracking (MPPT) techniques. To identify the most suitable technique that can be implemented practically, we conduct a comparative study in this paper between MPPT algorithms, namely Incremental Conductance (INC), Perturb and Observe (P&O), Fuzzy Logic (FL), and Artificial Neural Network (ANN). Using MATLAB/Simulink, our study was conducted under the same operating conditions, with a focus on efficiency, statistical analysis of robustness, and computational complexity. Our results show that the FL controller delivered the best overall performance, whose effectiveness depends on the accuracy of the rule base and scaling factors. It is characterized by a mean efficiency of 97.17%, a rapid response of 0.0585 s, minimal steady-state oscillations, and strong adaptability to environmental variations. The ANN-based approach achieves a mean efficiency of 94.91% and exhibits high performance at medium to high irradiance levels. However, its efficiency decreases significantly at low irradiance, resulting in reduced stability and increased deviation. INC and P&O achieve mean efficiencies of 95.20% and 95.15%, respectively. Moreover, due to their low computational cost, both techniques can be easily implemented. However, under rapidly changing conditions, they exhibit slower dynamics and more pronounced oscillations around the maximum power point, resulting in less stability.OPEN ACCESS Received: 01/08/2025 Accepted: 14/10/2025 Published: 23/01/2026
Abstract
Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extraction under dynamic [...]
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Reliability Modeling Using Weibull Distribution in Partially Accelerated Life Tests with Unified Hybrid Censoring: White Organic LEDs and Micro-Droplet Applications
O. Alqasem, M. Nassar, M. Elwahab, A. Elshahhat

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 22

Abstract
Accelerated life testing has become essential in modern reliability assessment, particularly for high-reliability products where traditional testing is often impractical due to time constraints. This study introduces a comprehensive framework for reliability analysis using Weibull-based partially accelerated life tests under a unified hybrid censoring scheme. Assuming lifetimes follow a Weibull distribution, we provide both classical and Bayesian estimation procedures to estimate core quantities, shape, scale, and the acceleration factor, alongside reliability metrics at normal operating conditions. The inferential framework encompasses point estimation along with uncertainty quantification, using both approximate confidence intervals and Bayesian credible intervals derived from Markov Chain Monte Carlo methods. A comprehensive Monte Carlo simulation study evaluates the performance of various methods in terms of mean squared error, interval coverage, and average interval width across a range of censoring patterns. The results provide actionable insights and practical recommendations for selecting appropriate methods and designing future studies under different censoring scenarios. The proposed methodology is further illustrated through two real-world case studies: the reliability of white organic light-emitting diodes and the lifetime of micro-droplets in ambient environments. These examples highlight the method’s flexibility and practical relevance in both engineering and biomedical reliability applications.OPEN ACCESS Received: 31/07/2025 Accepted: 10/09/2025 Published: 23/01/2026
Abstract
Accelerated life testing has become essential in modern reliability assessment, particularly for high-reliability products where traditional testing is often impractical due [...]
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Study on the Annular Fluid Level IdentificationAlgorithmfor Ultra-Deep Wells Based on AcousticVelocityCorrection
K. Liu, C. Yang, M. Li, S. Tang, W. Hao, W. Wang, Y. Chen, Y. Li

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 21

Abstract
The geological conditions and pressure system of ultra-deep oil and gas wells are complex, and formation leakage conditions are prone to occur. Accurately obtaining the annular liquid level depth during leakage is crucial for treatment decisions. In the high-temperature and high-pressure wellbore environment of ultra-deep wells, the sound velocity shows a nonuniform variation trend, which seriously affects the accuracy of annular liquid level identification. Therefore, it is of significance to carry out a study on the annular fluid level identification algorithm based on acoustic velocity correction. Based on establishing a calculation model for the annular temperature and pressure field, a sound velocity calculation model in the annular air section was constructed. By combining the actual sound velocity of the clear signal segment identified through the echo signal, the basic parameters in the sound velocity calculation model can be calibrated to obtain the calibrated wellbore sound velocity distribution. Finally, tests were conducted on simulated well sites and noisy production wells to verify the accuracy of the annular liquid level identification algorithm constructed in this study. The results showed that the identification algorithm had an error of less than 2%. All in all, this study can effectively meet the demand for dynamic annular liquid level data during the leakage conditions, which is of great significance for the treatment and decisionmaking in deep and ultra-deep wells.OPEN ACCESS Received: 30/07/2025 Accepted: 25/09/2025 Published: 23/01/2026
Abstract
The geological conditions and pressure system of ultra-deep oil and gas wells are complex, and formation leakage conditions are prone to occur. Accurately obtaining the annular [...]
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AI-Driven Modeling of Window Opening Behavior in Kindergarten Classrooms: A Case Study during the Transitional Season in the Cold Region of China
C. Zhang, W. Chong, B. Chew, S. Pan, J. Wu, M. Qin, L. Cheng, F. Wang

Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (1), 13

Abstract
With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational control of window opening behavior (WOB) has become a crucial issue in building design and environmental management. However, existing research primarily focuses on buildings for adults, with relatively few studies on buildings for children, particularly those used by children aged 3–6. Moreover, previous studies often overlook the impact of functional differences between buildings on occupant behavior patterns. This study focuses on a kindergarten and proposes an event-based method for analyzing and modeling WOB. The results show that events such as arrival, class, and departure are associated with higher frequencies of window opening (exceeding 50%), whereas events such as dietary activity, indoor/outdoor activity, sleep, and tidying exhibit lower probabilities. WOB is more sensitive to indoor air quality during events with higher student activity (e.g., class, dietary activity, and indoor activity), resulting in more frequent ventilation. In terms of modeling, the Random Forest (RF) algorithm achieved higher prediction accuracy than Logistic Regression (LR) and Support Vector Machine (SVM). To reduce the complexity associated with multi-model integration, a stacking model was introduced, further enhancing predictive performance. Finally, the generalizability of the proposed method was validated using office building data from the ASHRAE occupant behavior database, achieving a maximum accuracy improvement of 3.87%. This study presents a novel approach for modeling WOB in functional buildings such as kindergartens and provides theoretical support for energy efficiency optimization and indoor air quality management.
Abstract
With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational control of window opening [...]
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