Advances in Engineering Software最新文献

{"title":"Optimization design and simplified model of a multi-layered nested tubular structure for train collision protection","authors":"Jun Chen ,&nbsp;Biao Wei ,&nbsp;Lizhong Jiang ,&nbsp;Xianglin Zheng ,&nbsp;Shuaijie Yuan ,&nbsp;Mingyu Chen","doi":"10.1016/j.advengsoft.2025.104039","DOIUrl":"10.1016/j.advengsoft.2025.104039","url":null,"abstract":"<div><div>Rising incidence of train derailments and collisions underscores the urgent need for more effective passive energy‐absorbing systems. While conventional aluminum honeycomb devices achieve high specific energy absorption, they suffer from complex fabrication, require full replacement after minor impacts, and generate high rebound velocities that can exacerbate secondary damage. In this study, we propose a Multi-layered Nested Tubular Structure (MNTS)—an arrangement of adjustable square and circular thin-walled tubes—as an alternative absorber. A physics–based finite‐element (FE) model, incorporating material nonlinearity, simulates a lead‐car collision against a rigid wall and is validated against full-scale experiments (velocity: 8.357 m/s; mass: 54 t). The model accurately reproduces peak absorbed energy, average force response, displacement history, and rebound velocity. A parametric study of 144 FE simulations combined with response surface methodology identifies optimal wall‐thickness parameters (<em>λ_s</em> = 7.4 mm, <em>λ_c</em> = 18.6 mm), yielding a maximum energy absorption of 1.728 MJ (<em>R_MSE</em> = 0.0477 MJ, <em>R</em>² = 0.945). Building on these results, we develop a reduced‐order analytical model using logistic regression to relate train speed (5.0–9.0 m/s) to peak force, maximum displacement, and energy absorption, achieving an <em>R</em>² of 0.989 for displacement predictions. Validation against 41 additional FE runs confirms the analytical model’s accuracy while reducing computational cost by orders of magnitude. Compared with honeycomb absorbers, the MNTS matches energy-absorption efficiency yet significantly lowers peak impact forces and rebound velocities, thereby enhancing passenger safety. Together, the validated FE framework and its streamlined analytical counterpart constitute a rapid, practical design and assessment tool for train collision energy-absorption systems.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104039"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on coal wall parameter calibration and high precision model construction based on discrete element method","authors":"Xin Jin ,&nbsp;Dongpo Han ,&nbsp;Guochao Zhao ,&nbsp;Lijuan Zhao","doi":"10.1016/j.advengsoft.2025.104032","DOIUrl":"10.1016/j.advengsoft.2025.104032","url":null,"abstract":"<div><div>The accuracy of discrete element coal wall model significantly influences the characterization of coal-rock breaking mechanisms and equipment dynamic response in virtual prototype simulation. Based on coal-rock samples from Ordos Wenyu Mine of Yanzhou Coal Mining, key Tavares UFRJ parameters affecting particle compressive strength were identified through Plackett-Burman test and steepest ascent experiment. Breakage parameters were calibrated using optimal latin hypercube sampling (OLHS) and gaussian process regression (GPR). Hertz-Mindlin with Bonding parameters were then calibrated via uniaxial compression simulation. Model accuracy was verified through discrete element method-multi flexible body dynamics (DEM-MFBD) coupling simulation. Results indicate that D0, E Infinity, and Phi are the most significant parameters with influence rates of 38.5 %, 30.5 %, and 18.6 % respectively. The relative error between simulated and experimental particle compressive strength is below 4.56 %, while uniaxial compression simulation shows maximum relative error below 9.80 %. Comparing tri-axial load curves during shearer drum cutting, the maximum relative error of mean values between experimental and simulation data is 3.72 %, with maximum root mean square error (RMSE) of 4.60 %, outperforming traditional models and validating the model's accuracy and reliability for dynamic cutting process simulation.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104032"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mooring tension estimation for multi-connected floating photovoltaic arrays via LSTM networks","authors":"Jihun Song ,&nbsp;Chungkuk Jin ,&nbsp;Do Kyun Kim ,&nbsp;Donghwi Jung ,&nbsp;Seungjun Kim","doi":"10.1016/j.advengsoft.2025.104037","DOIUrl":"10.1016/j.advengsoft.2025.104037","url":null,"abstract":"<div><div>Accurately estimating mooring‑line tension is essential for the safe operation of large, multiconnected floating‑photovoltaic (FPV) arrays, yet installing load cells on every line is impractical. This study develops and evaluates data‑driven tension estimators that use only motion responses generated from time‑domain hydrodynamic simulations. A long short‑term memory (LSTM) network trained on displacements provides the reference performance. When trained instead on raw accelerations, the model performs noticeably worse, reflecting the spectral mismatch between acceleration and tension signals. Adding directional spreading to the training data restores robustness for the displacement‑based model under oblique seas, but offers limited benefit for the acceleration‑based model. In this study, a physics‑guided LSTM is proposed to reduce reliance on displacement sensors, in which a learnable filter transforms accelerations into displacement‑like features. This hybrid model narrows the performance gap, achieving stable and robust prediction performance. The proposed model attains accuracy comparable to displacement‑based estimation, demonstrating its effectiveness with accelerometer input alone and highlighting its potential as a cost‑efficient tool for structural health monitoring of large‑scale FPV systems.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104037"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic performance comparison of passive and active friction-tuned mass dampers in tall buildings considering soil-structure interaction effects","authors":"Morteza Akbari ,&nbsp;Abbas-Ali Zamani ,&nbsp;Tomasz Falborski ,&nbsp;Sadegh Etedali ,&nbsp;Robert Jankowski","doi":"10.1016/j.advengsoft.2025.104036","DOIUrl":"10.1016/j.advengsoft.2025.104036","url":null,"abstract":"<div><div>This study investigates the enhancement of seismic performance in tall buildings by utilising passive friction-tuned mass dampers (PFTMDs), with a specific emphasis on the impact of soil-structure interaction (SSI). The novelty of this work lies in integrating multi-objective optimization of PFTMDs with consideration of SSI effects, providing a comprehensive comparison with active damping strategies under various soil conditions. A multi-objective marine predator algorithm (MOMPA) is employed to optimize the damper parameters. The analysis incorporates four earthquake ground motion records and three soil types: soft, medium, and dense. The optimized performance of the PFTMD is compared to that of an active friction-tuned mass damper (AFTMD), which is controlled by a proportional-integral-derivative with filter (PIDF) controller, also optimized via MOMPA. Both control strategies are benchmarked against an uncontrolled structure. The results indicate that both systems significantly enhance the seismic performance of the structure by reducing displacement, acceleration, and inter-storey drift. However, the AFTMD consistently outperforms the PFTMD across all soil conditions. To account for the unpredictability of seismic events, the study further assesses damper performance using averaged parameters derived from multiple earthquake-soil combinations. While the PFTMD performs effectively when tuned for specific conditions, its performance declines under averaged settings and can even amplify structural responses. Conversely, the AFTMD shows robust and consistent performance, establishing it as a more reliable solution for seismic mitigation in tall buildings.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"211 ","pages":"Article 104036"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic vibration mechanisms in irregular coupled plate of supersonic porous functionally graded materials with temperature gradients","authors":"Hui Dong ,&nbsp;Rui Zhong ,&nbsp;Qingshan Wang ,&nbsp;Tao Liu ,&nbsp;Long Yu","doi":"10.1016/j.advengsoft.2025.104002","DOIUrl":"10.1016/j.advengsoft.2025.104002","url":null,"abstract":"<div><div>The porous irregular functional gradient material (FGM) coupled plates, composed of two arbitrary quadrilateral plates coupled at any angle, are widely used in aerospace applications and equipment such as hypersonic vehicles. This paper investigates the stochastic response mechanisms of the porous irregular FGM coupled plate under aerothermal environments and base acceleration excitations. Three typical geometric models are established to validate the universality of the present method. The equations derived from supersonic piston theory and Mindlin plate theory incorporate temperature-dependent material properties. Subplate displacements are approximated using the first-kind Chebyshev polynomials, with irregular domain integrals resolved through coordinate transformations. Sufficient comparisons with the finite element method (FEM) and published literature confirm the accuracy and computational efficiency of this approach. The resulting systematic framework enables stochastic response analysis in analogous complex structures. Numerical discussions are conducted to analyze the effects of FGM gradient <em>p</em>, porosity <em>ζ</em>, coupling angle <em>θ</em>, boundary conditions, and temperature variations Δ<em>T</em> on the stochastic response, establishing practical tools for optimizing and conducting rapid integrity assessment of such structures.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104002"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization design method of C30/C40 fly ash concrete based on machine learning and elite retention genetic algorithm","authors":"Mingyue Hao ,&nbsp;Yue Li ,&nbsp;Xiwang Chen ,&nbsp;Kun Ni ,&nbsp;Wei Li","doi":"10.1016/j.advengsoft.2025.104019","DOIUrl":"10.1016/j.advengsoft.2025.104019","url":null,"abstract":"<div><div>This paper establishes an intelligent optimization design method for fly ash (FA) concrete considering 28-day compressive strength, slump, and carbon emissions based on machine learning (ML) and elite retention genetic algorithm (EGA). The results demonstrate that the Extreme Gradient Boosting (XGB) model achieves high accuracy in predicting compressive strength, while Gradient Boosting (GB) shows higher accuracy and generalization ability in predicting slump. The water-to-binder ratio and cement content have a significant impact on the compressive strength of FA concrete. Reducing the water-to-binder ratio or increasing cement content helps improve compressive strength. The dosage of superplasticizer and the water content are key factors in controlling the slump. Properly increasing the dosage of superplasticizer and water content can effectively improve the slump of concrete. The FA concrete intelligent design system developed based on the XGB model, GB model, and EGA algorithm can efficiently obtain the optimal preparation parameters and accurately predict the corresponding performance. Furthermore, the carbon emissions of the optimized C30 and C40 FA concrete decrease by 12.72 % and 17.44 % respectively compared to the baseline concrete. Finally, the experimental results verify the prediction accuracy and generalization ability of the XGB and GB models, with the relative prediction error of C30 and C40 FA concrete both being less than 8 %.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104019"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Less than 500 lines self-contained Python finite element implementation of the phase-field method for fracture mechanics","authors":"Nathan Shauer","doi":"10.1016/j.advengsoft.2025.104013","DOIUrl":"10.1016/j.advengsoft.2025.104013","url":null,"abstract":"<div><div>This paper presents a simple self-contained finite element implementation of the phase-field method for fracture mechanics. The implementations are done in Python, and they only use the standard <span>NumPy</span> and <span>SciPy</span> libraries for basic matrix operations and to solve the resulting systems of equations. The AT2 phase-field model is adopted and the additive decomposition of the energy density is employed to prevent fracture propagation under compressive stresses. The alternate minimization algorithm is adopted for solving the nonlinear system of equations. The implementation is verified using three examples: a bar under tension, a notched plate under tension, and a three-point bending test. The results display good agreement with analytical solutions and solutions from other authors. Each example is less than 500 lines long, and they are available on GitHub at <span><span>https://github.com/nathanshauer/phasefield-jr-py</span><svg><path></path></svg></span> and as supplementary data to this article. These Python scripts are intended for educational purposes and to provide a simple starting point for those interested in the phase-field method for fracture mechanics.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104013"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CH:ALK - Rapid automatic labeling toolkit to develop training images for concrete damage segmentation models","authors":"Hyojae Shin ,&nbsp;Byunghyun Kim ,&nbsp;Soojin Cho","doi":"10.1016/j.advengsoft.2025.104010","DOIUrl":"10.1016/j.advengsoft.2025.104010","url":null,"abstract":"<div><div>With the growing demand for automated structural inspection due to the aging of civil infrastructure, deep segmentation models have been increasingly adopted with the imaging of structures. However, training the models using common supervised learning requires labeled data, and traditional manual labeling is labor-intensive, inconsistent, and time-consuming. This study introduces CH:ALK (Concrete Highlighter: Accelerated Labeling Toolkit), a rapid labeling toolkit designed to produce fast, accurate, and consistent training images for supervised learning of damage segmentation models. CH:ALK integrates automatic labeling (AL) using pre-trained CGNet (Context-Guided Network) and SAM (Segment Anything Model) to label four types of concrete damage: cracks, efflorescence, rebar exposure, and spalling. CH:ALK supports pixel-level AL that can be followed by manual correction via brush tools in an intuitive GUI. Performance validation using 80 images labeled by four users demonstrated an average time reduction of 87.97 %, accuracy of 67.07 % (mIoU), and inter-user consistency of 78.44 %, compared with traditional manual labeling (ML). Furthermore, two segmentation models, CGNet and DeepLabV3+, trained with AL data showed comparable performance to those trained with ML data. CH:ALK offers a scalable solution for developing high-quality labeled datasets for civil infrastructure inspection.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104010"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the effectiveness of multigrid preconditioned iterative methods for large-scale frequency response topology optimization problems","authors":"Yongxin Qu ,&nbsp;Niels Aage ,&nbsp;Quhao Li","doi":"10.1016/j.advengsoft.2025.104017","DOIUrl":"10.1016/j.advengsoft.2025.104017","url":null,"abstract":"<div><div>Large-scale static topology optimization of mechanical structures has been successfully realized for linear problems, including giga-voxel resolution aircraft wings and suspension bridges. Wherein the multigrid preconditioned conjugate gradient method (MG-CG) plays an important role in the repetitive solution of the state equations. However, research on large-scale dynamic topology optimization, e.g., frequency response problems, is still limited. Since the coefficient matrix of the dynamic equation is no longer a positive definite symmetric matrix, yet an indefinite, non-Hermitian and complex matrix, the conjugate gradient method (CG) is no longer applicable and the standard weapon-of-choice, the geometric multigrid preconditioner is no longer guaranteed to work. It is therefore of interest to investigate which iterative methods, if any, posses excellent generality and low computational-cost. In this paper, the effectiveness of several typical preconditioned iterative methods is studied, including conjugate gradient method, biconjugate gradient stabilized method (BICGSTAB), induced dimensionality reduction (IDR), generalized minimum residual method (GMRES). A detailed comparison and analysis of iterative methods' convergence, mesh dependence, and sensitivity to stiffness distribution in dealing with indefinite problems is given first. Then, despite its known disabilities, the geometric multigrid method is applied as a preconditioner for GMRES, BICGSTAB and IDR, i.e., MG-GMRES, MG-BICGSTAB and MG-IDR, to facilitate the efficient solution of large-scale frequency response analysis. In addition, the influence of several smoothers, including damped Jacobian iteration, successive over relaxation, symmetric SOR, and incomplete LU factorization, on the convergence of geometric multigrid iterative methods is also discussed. Numerical examples show that MG-BICGSTAB deals with low-frequency problems well, but for the whole frequency range, MG-GMRES with ILU smoother converges quickly and steadily, even if the model is extremely large. Furthermore, the effectiveness of the proposed procedure is further verified in dynamic topology optimization with up to 2.8 million degrees of freedom using a standard desktop computer.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104017"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning-based inverse analysis procedure for concrete softening law prediction using non-experimental datasets","authors":"H. Rodrigo Amezcua ,&nbsp;A. Gustavo Ayala ,&nbsp;Carlos E. González","doi":"10.1016/j.advengsoft.2025.104016","DOIUrl":"10.1016/j.advengsoft.2025.104016","url":null,"abstract":"<div><div>This paper studies the mechanical behaviour of concrete as one of the most widely used quasi-brittle construction materials emphasizing on the importance of knowing its mechanical parameters and their evolution during the inelastic stage, <em>i.e.</em>, the softening law. The softening curve, which describes the response of the material under damage or cracking, is critical for predicting the behaviour of concrete structures subjected to extreme loads. Experimental tests are commonly employed to obtain this information either directly or indirectly. Some of the indirect methods are based on inverse analysis and/or artificial intelligence techniques, both of which capable of predicting the mechanical parameters of concrete from the experimental results of one test, <em>e.g.</em>, a notched beam subjected to vertical loads. However, an important drawback of these procedures is that they require a large dataset constructed from data gathered in multiple experiments in order to be developed. Consequently, most existing methods are tailored to specific types of experiments and even limited to certain specimen dimensions. Additionally, these procedures primarily focus on predicting mechanical parameters rather than determining the softening law. To address these limitations, this paper proposes a machine learning-based algorithm for the inverse analysis of an experimental test capable of predicting both the softening law and the mechanical parameters of concrete. By generating a non-experimental dataset through the Sequentially Linear Analysis (SLA) procedure, the proposed algorithm can be applied to other experimental setups suitable for analysis with SLA. The results of the application example demonstrate the effectiveness of the proposed approach.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"210 ","pages":"Article 104016"},"PeriodicalIF":5.7,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}