Documents published in Scipedia

• Deep Learning Approaches for Hydrological Forecasting: A Systematic Review

  L. Singh, K. Singh, K. Robindro, N. Hoque, D. Bhattacharyya

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 62

  
  

  Abstract

  Machine learning (ML) techniques, especially Deep Learning (DL) techniques, have been applied for flood risk analysis and prediction on spatial historical data to minimize the risk of the loss of lives and properties associated with floods. In recent years, various studies have established DL as an effective approach for building potential flood prediction models. However, a thorough, systematic integration of recent advancements in DL for hydrological forecasting, their reported performance, and challenges is essential for guiding future work effectively. This paper presents a systematic survey of various DL models applied to flood prediction. This systematic review integrates studies published between 2018 and 2025 that used DL models in hydrological forecasting, such as flood prediction, streamflow forecasting, and runoff modeling. A systematic search of major electronic databases was performed using pre-defined inclusion and exclusion criteria. The overall search provided 647 records, which were narrowed down to a final collection of 45 studies after screening and full-text examination based on factors like study design, hydrological forecasting relevance, and application of DL. The review quantitatively summarizes the reported performances of various DL models, including RNN variants (LSTM, GRU), CNN, GAN, and hybrid architectures, across different hydrological forecasting tasks and datasets. Findings indicate that DL models consistently achieve high performance metrics such as NSE of (0.99), RMSE of (24.61) and MAPE of (1.73) for certain applications. Despite these advancements, significant research gaps remain, particularly concerning the scarcity of high-quality, publicly available datasets with detailed spatial information, the need for more robust real-time prediction systems with minimized false alarms, and the development of more generalized models applicable across diverse geographical regions. this review highlights the significant potential of DL in hydrological prediction as well as clearly stating the fundamental challenges that need to be overcome in order to achieve more robust and generalizable flood prediction systems.OPEN ACCESS Received: 25/04/2025 Accepted: 22/07/2025 Published: 27/10/2025

  Abstract
  Machine learning (ML) techniques, especially Deep Learning (DL) techniques, have been applied for flood risk analysis and prediction on spatial historical data to minimize [...]

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• Inference of the Exponentiated Rayleigh Distribution on Step-Stress Accelerated Life Testing under Type-I Hybrid Censored Data with Physical Application

  M. Fawzy, I. Alasbahi, T. Alof, A. Ahmad, H. Taha, A. El-Bary, K. Lofty

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 61

  
  

  Abstract

  This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model. The maximum likelihood and Bayes methods estimate the parameters and acceleration factor. The parameters’ approximate confidence intervals are created. The Bayes estimates of the parameters for the squared error and linear exponential loss functions are computed using the Markov Chain Monte Carlo (MCMC) method. Finally, we perform a simulation study to evaluate the effectiveness of the proposed estimators. We provide a real-life data example (Strength data measurement in GPA, for single and impregnated carbon fibers) to explain the obtained results.OPEN ACCESS Received: 15/04/2025 Accepted: 21/08/2025 Published: 27/10/2025

  Abstract
  This paper aims to estimate the unknown parameters for the exponentiated Rayleigh distribution using Type-I hybrid censored data under a step-stress model. The maximum likelihood [...]

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• Effects of the Trapezoidal Embedded Loading Berm on the Stability of Soft-Soil Foundation Embankment

  F. Xiong, Y. Wang, F. Yang, L. Zhou, C. Hu, X. Wang, F. Li

  Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (4), 60

  
  

  Abstract

  The traditional loading berm is effective in reinforcing soft-soil foundation embankments, however, it is found that its large footprint limits its application in some narrow construction sites in recent years. Therefore, to address this limitation, the trapezoidal embedded loading berm (TELB) has been introduced, and its feasibility was examined in this paper. Firstly, an analytical model was developed to investigate the mechanical behavior of the TELB. Then, a numerical approach was employed to assess the TELB’s efficacy in enhancing the stability of soft-soil foundation embankments. The study elucidates the impact of various TELB parameters— such as slope angle, lower edge width, height, density, internal friction angle, and cohesion—on the embankment stability coefficient. Finally, an orthogonal test was conducted to evaluate the sensitivity of each parameter concerning embankment stability. The results demonstrate that the TELB substantially improves the stability of soft-soil foundation embankments, with stability coefficients increasing as the geometric and physicalmechanical parameters of the loading berm are enhanced. Among the parameters, height and density exert a more pronounced effect on the stability coefficient compared to cohesion and internal friction angle. This research provides valuable insights for the design and construction of TELBs and contributes to mitigating the environmental impact of road construction in soft soil regions.OPEN ACCESS Received: 23/03/2025 Accepted: 10/06/2025 Published: 27/10/2025

  Abstract
  The traditional loading berm is effective in reinforcing soft-soil foundation embankments, however, it is found that its large footprint limits its application in some narrow [...]

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• Personalized design and reconstruction for the defect bone based on data driving and mechanics modelling

  Z. Zhuang

  Complas2025.

  
  

  Abstract

  Human periarticular bone defect is a difficult disease in orthopedics. There is challenge issue to recognize anisotropy, heterogeneity of bone tissue structure and graphics by low resolution clinic-CT image. In collaboration with clinical medicine, the data-driven and mechanics modeling technique for bone defect reconstruction is proposed. Data driven micro-CT and clinical-CT images are used to obtain the characteristics of cancellous bone structure and graphics. The experimental technology and numerical method are developed for predicting the mechanics parameters of animal specimen on the multi-axial stress state. The constitutive model of heterogeneous anisotropy of bone tissue is established and the parameters are deduced by Bayesian inference using the data given by numerical simulation and experiment. For designing the robust cancellous prosthesis bone, a kind of spinodal lattice is designed with random, indeterminate, aperiodic, asymmetry, irregular, large space for mechanical and biological function. The digital triplets with physical scanning CT image, virtual equivalent modulus and additive manufacturing lattice design are created to guide the clinical treatment of personalized bone defects. This work has been demonstrated in some clinical applications to the benefit of patients.

  Abstract
  Human periarticular bone defect is a difficult disease in orthopedics. There is challenge issue to recognize anisotropy, heterogeneity of bone tissue structure and graphics [...]

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• Constitutive Laws as Generative Graphs and Trees

  W. Sun

  Complas2025.

  
  

  Abstract

  This talk explores the various ways high-fidelity constitutive laws for a wide range of solids, such as soil, rock, alloys, and polymer composites, can be represented and how the choice of representations influences the accuracy, robustness, and data/computational efficiency for computer simulations of solids. To represent material models as points, we adopt a model-free approach that enables physical simulations of material behaviors without a smooth constitutive law. In this case, pointwise stress-strain pairs are selected in Gauss points of finite elements to be compatible with the conservation laws. To represent material models as meshes, we introduce a latent diffusion model where previous material models and experimental data are used to guide the reverse generation of models. This mesh-based material model is particularly efficient for non-smooth plasticity, where projection on segments can lead to significantly faster simulations. To represent material models as expression trees, we use the neural additive model in the projected space of strain measures. This technique enables us to search for hyperelasticity in high-dimensional space without sacrificing the expressivity of neural networks. We show that the proposed model may reproduce any polynomial of arbitrary orders and dimensions and thus achieve the universal approximation through the StoneWeierstrass theorem. Through a series of 1D post-hoc symbolic regressions, we obtain symbolic material models that significantly reduce the inference time for hydrocodes [1]. The pros and cons of these techniques for various practical applications will be discussed. 

  Abstract
  This talk explores the various ways high-fidelity constitutive laws for a wide range of solids, such as soil, rock, alloys, and polymer composites, can be represented and [...]

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• On the application of the virtual element method for problems with inelastic material behaviour

  P. Wriggers

  Complas2025.

  
  

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• Plasticity and the airframe -- aircraft structure from birth to ultimate state

  S. Van-der-Veen

  Complas2025.

  
  

  Abstract

  From the beginning of metallic airframes in the 1930's until today, it has always been required to have a thorough understanding of the plastic behaviour of aircraft structure. Nowadays, advanced models of plasticity are used along the entire design and development process: they are needed to compute the ultimate state of the structure for certification, and also to simulate the manufacturing of the often complex, double-curved components.    Mastery of this chain has helped to create the safest mode of transport humankind has known so far.    This talk will recap some of the history that got us to where we are today, to help understand current practice, and it will sketch future needs and possibilities.    As in the past, the collaboration between academic research and the aerospace industry will continue to be the key to safety, comfort and sustainability.

  Abstract
  From the beginning of metallic airframes in the 1930's until today, it has always been required to have a thorough understanding of the plastic behaviour of aircraft structure. [...]

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• Surrogate computational homogenization for inelastic composites and room for quantum acceleration strategies

  K. Terada

  Complas2025.

  
  

  Abstract

  We have recently proposed a class of data-centric methods for computational homogenization (CH) using radial basis function (RBF) interpolation, which substitutes for the microscopic analysis for inelastic composites at small-strain and finite-strain [1,2,3]. This approach, which is referred to as RBF-based surrogate CM, has been applied to elastoplastic composite materials at small strain [1] and finite strain [2], and viscoelastic composite materials [3] to demonstrate the capability in overcoming the difficulty of conventional multiscale analysis methods. However, the computational cost of the process of obtaining the weights of RBFs by solving linear equations with the kernel matrix as coefficients is high, and this approach has been applied only to twodimensional (2D) problems. To address the problem that comes from the sheer volume of data, in this study, various measures can be taken to extend the RBF-based surrogate CH to 3D problems from a practical perspective. For example, a limited number of combinations of the six components of macroscopic strain are randomly selected on the hypersphere to perform numerical material tests to generate a set of macroscopic stresses and macroscopic strain histories. After that, the procedure should be the same as the one we have developed so far. The present study particularly focusses on a partitioned RBF interpolation with the help of decision-tree-based partitioning of the data space. Optimizing the partitioning of the hyperspace, consisting of macrostrain and historydependent variables in the input data, allows for low-cost and efficient RBF interpolation approximation. Several numerical examples are presented to demonstrate the promise and performance of the proposed method. The RBF-based surrogate model thus created can be applied to several engineering problems. For example, its convenience can be used for topology optimization, and we will also briefly discuss its potential for combination with quantum algorithms for nonlinear multiscale analysis. 

  Abstract
  We have recently proposed a class of data-centric methods for computational homogenization (CH) using radial basis function (RBF) interpolation, which substitutes for the [...]

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• Advances and structural applications of Isogeometric Analysis

  A. Reali

  Complas2025.

  
  

  Abstract

  Isogeometric Analysis (IGA) is a successful simulation framework originally proposed by T.J.R. Hughes et al., in 2005, with the aim of bridging Computational Mechanics and Computer Aided Design. In addition to this, thanks to the high-regularity properties of its basis functions, IGA has shown a better accuracy per degree-of-freedom and an enhanced robustness with respect to standard finite elements in many contexts - ranging from solids and structures to fluids, as well as to different kinds of coupled problems – opening also the door for the approximation in primal form of higher-order partial differential equations. After a concise introduction of the basic isogeometric concepts, this lecture aims at presenting some IGA recent advances with a special focus on interesting structural applications in several fields where the characteristics of IGA seem to be of great advantage. In particular, applications that will be discussed include the simulation of fluid-structure interaction in different situations, studies on the effect of mechanicallyinduced stresses on prostate cancer growth, thermo-mechanical simulations of additive manufacturing processes, electro-mechanical simulations for biological tissues, and the use of phase-field modeling of fracture and other problems.

  Abstract
  Isogeometric Analysis (IGA) is a successful simulation framework originally proposed by T.J.R. Hughes et al., in 2005, with the aim of bridging Computational Mechanics and [...]

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• Recent advances in the particle finite element method for fluid-structure interactions and multi-physics problems

  J. Ponthot

  Complas2025.

  
  

  Abstract

  Particle Finite Element Method (PFEM) is a still rather young discretization method that seeks to merge the advantages of the classical FEM with those of modern particle-based methods, such as SPH. To this end, PFEM is designed as a Lagrangian method that combines computations over one time step using FEM with a fast remeshing algorithm, thereby avoiding mesh distortions consequent to very large deformations, such as those encountered in fluid flow with free surfaces. The method is thus quite flexible and can be applied to both solid and fluid material behavior (see e.g. [1] as a sample of the state of the art). PFEM has proven to be a very versatile method that not only allows tracking free evolving boundaries but also take into account thermo-mechanical coupling and thus tackle more complex multi-physics problems. For instance, thanks to its Lagrangian character and its ability to automatically track evolving free surfaces and interfaces, PFEM allows handling phase change due to solidification, melting and vaporization, as well as capillary and Marangoni effects from surface tension [2]. These physical ingredients are highly valuable for simulating melt pool dynamics, for example, in the context of additive manufacturing.  Multiphysics problems can lead to models that are inherently incompatible or highly difficult to combine within the same numerical implementation. In such a case, it is convenient to split the physical models and solve them separately using dedicated software. Although this approach has been used mostly to address fluid-structure interaction problems in PFEM, it has also been used to couple thermo-mechanical models, for example, in the simulation of welding processes. This work gathers recent advances in the PFEM with special attention to the incorporation of multi-physics models and their applications. In addition, numerical examples of the PFEM will illustrate fluid-structure interaction problems including contact between different solid parts and plastic deformation of some components of the system. These advances will be complemented by new remeshing proposals to further improve the PFEM strategy, which aim at reducing numerical artifacts and improving the continuity and smoothness of the free surface on which complex physical phenomena take place.

  Abstract
  Particle Finite Element Method (PFEM) is a still rather young discretization method that seeks to merge the advantages of the classical FEM with those of modern particle-based [...]

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