Abstract

In managing strong roof loading in steep-inclined longwall panels, this study adopts partial gob backfill mining along the dip direction. Four controlling factors for roof deformation are identified: working face length (L), mining depth (H), seam dip angle (α), and backfill length (a). Parametric analysis determines that L = 105 m combined with a 2/5 backfill ratio achieves optimal strata control. Physical experiments recorded dip-direction stress gradients: upper (8.65/7.79/8.45 MPa), central peak (9.86/9.15/9.86 MPa), and lower (8.82/8.41/8.83 MPa), with displacement increments of horizontal (+115.6%/+73.9%/+74.1%), vertical (+136.2%/+48.9%/+21.3%), and resultant (+80.6%/+94.8%/+39.2%). FLAC3D simulations systematically varied backfill ratios (1/5, 2/5, 3/5) and face lengths (90, 105, 120 m). Increasing the ratio from 1/5 to 2/5 reduced peak stress by 7.7% (15.65 → 14.45 MPa) and subsidence by 39.3% (1.78 → 1.08 m), while further increase to 3/5 yielded marginal gains (4.5%, 31.5%). At the optimal 2/5 ratio, extending face length from 90 to 105 m increased abutment stress by 8.9% (13.27→14.45 MPa) and subsidence by 17.4% (0.92→1.08 m), while 120mcaused disproportionate surges (5.2%, 49.1%) with plastic zone height soaring 81.9% (36.05→ 65.56 m). Under the optimal 105 m–2/5 configuration, staged advance (20–80 m) quantified progressive stress transfer: lower-end pillar stress rose 20.4% (9.22→11.10MPa), backfill stress 24.7% (8.75→10.91MPa), and roof subsidence from 302 to 688 mm, with plastic zone evolving as an asymmetric arch characterized by shear failure at the arch foot (lower pillar/backfill interface) and tensile failure at the crown. This integrated approach confirms that partial backfill effectively regulates strata behavior, providing a quantitative framework for sustainable steep-seam mining.

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 Published: 20/03/2026

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

Bridges are key projects of high-grade Expressways in mountainous areas. The verticality of bridge piers with a height of more than 100 m is crucial to ensure the safety and stability of bridge projects. When a pier construction is completed but the upper beam structure has not yet been connected (socalled after construction or constructed), the verticality of the pier is most likely to vary due to some factors. Based on the Zhaidi River Bridge project of Yunnan Zhenhe Expressway, considering the natural environment of the bridge site, three wind force intensity levels (Beaufort Scale 8, 10, and 12) and two climate conditions (high temperature and high radiation in summer, and low temperature and low radiation in winter) were identified; and the wind-induced deformation, temperature-induced deformation, and wind-temperature coupled deformation of the constructed main pier of the Zhaidi River Bridge with a height of 112.60 m were simulated with ANSYS Workbench numerical simulation platform. The simulation results show that: the influence of wind on pier deformation is much greater than that of ambient temperature variation; the influence of solar radiation on temperature-induced deformation of the bridge pier is much greater than that of air temperature variation; the temperature-induced deformation of the pier body under low temperature and low radiation condition in winter is greater than that under high temperature and high radiation condition in summer; the directional effect of the superposition of wind-induced deformation and temperature-induced deformation is more significant under low temperature and low radiation condition in winter.OPEN ACCESS Received: 11/04/2025 Accepted: 16/10/2025 Published: 23/01/2026

Abstract

Steel partition walls are essential components in modern civil engineering, providing both structural support and spatial separation. These walls are frequently exposed to combined thermal and mechanical loads, particularly in specialized environments such as high-temperature workshops or fire scenarios, where their thermo-mechanical coupling behavior is critical to building safety and functionality. This study integrates the direct finite element squared (Direct FE2) method with generalized polynomial chaos expansion (PCE) to quantify the uncertainties in key material propertiesnamely, the elastic modulus and the coefficient of thermal expansionand to evaluate their effects on the thermo-mechanical performance of steel partition walls. The proposed approach enables efficient simulation of material uncertainties and their influence on structural behavior under coupled thermal-mechanical conditions. Case studies demonstrate both the accuracy and computational efficiency of the method, while sensitivity analysis highlights the most influential uncertainty factors. The integration of Direct FE2and PCE thus offers a robust framework for assessing the reliability of steel partition walls under uncertain conditions, providing valuable insights for design optimization and enhancing the safety and efficiency of building structures in practical applications.OPEN ACCESS Received: 05/07/2025 Accepted: 17/09/2025 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 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

This study evaluates the flexural behavior of an Ultra high performance fiber-reinforced concrete (UHPFRC) slab through experimental and Finite Element Method (FEM) analytical investigations. A full-size U-UHPFRC bridge deck specimen serves as a reference for the research. A nonlinear FEM is put forward to link material characteristics, failure mode, and bearing capacity of U-UHPFRC decks, considering the failure behavior with different impact parameters of reinforcement ratio, thickness and side ratio. The flexural performance calculation formula for UHPFRC slabs was derived using three failure modes. The results indicate that this method can effectively predict the load transfer and distribution patterns of UHPFRC thin slabs, providing a reference range for the reinforcement ratio, thickness and long-short side ratio in UHPFRC one-way or two-way slabs. These research results can optimize the crack resistance and toughness of thin UHPFRC decks, improve durability, and appropriately reduce carbon emissions. It is suitable for bridges or special structures with higher load requirements and provides theoretical support for the full-life operation and development of UHPFRC components.OPEN ACCESS Received: 31/05/2025 Accepted: 10/07/2025 Published: 27/11/2025