Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.compstruc.2026.108140
Xiao Huang , Kaiwen Guan , Takayuki Yamada
This study presents a topology optimization method considering visibility as a design requirement. To evaluate visibility, a fictitious physical model governed by an anisotropic steady-state advection–diffusion equation is constructed. Next, a topology optimization formulation is developed to integrate the proposed fictitious physical model into the optimization process. Then, the topological derivative for visibility analysis is derived using the adjoint variable method. In addition, a strategy is proposed to reduce computational costs by decreasing the size of the computational domain. Finally, the effectiveness of the proposed methodology and its constituent approaches is validated through several numerical examples.
{"title":"Topology optimization considering visibility based on a fictitious physical model","authors":"Xiao Huang , Kaiwen Guan , Takayuki Yamada","doi":"10.1016/j.compstruc.2026.108140","DOIUrl":"10.1016/j.compstruc.2026.108140","url":null,"abstract":"<div><div>This study presents a topology optimization method considering visibility as a design requirement. To evaluate visibility, a fictitious physical model governed by an anisotropic steady-state advection–diffusion equation is constructed. Next, a topology optimization formulation is developed to integrate the proposed fictitious physical model into the optimization process. Then, the topological derivative for visibility analysis is derived using the adjoint variable method. In addition, a strategy is proposed to reduce computational costs by decreasing the size of the computational domain. Finally, the effectiveness of the proposed methodology and its constituent approaches is validated through several numerical examples.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108140"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160922","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}
Pub Date : 2026-03-01Epub Date: 2026-02-13DOI: 10.1016/j.compstruc.2026.108145
Han-yun Liu, Hao-ye Shuai, Na Mao, Li-dong Wang, Yan Han, Peng Hu
This paper presents an intelligent optimization framework for multi-mode spanwise layout optimization of split-type wind fairings on long-span bridges to suppress vortex-induced vibration efficiently and economically. The formulation of spanwise layout optimization minimizes both total fairing length and spatial discontinuities while ensuring control effects across all critical structural modes. The multi-mode control criterion is derived from the condition that the critical total damping ratio of the bridge system equals zero. To satisfy this constraint, a modal energy control ratio is defined for each mode and regulated during optimization. Aerodynamic parameters are identified from segmental wind-tunnel tests on a cable-stayed bridge. Two intelligent optimization algorithms— backtracking greedy search and particle swarm optimization —are implemented, extending established single-mode strategies to the multi-mode context. The objective function prioritizes minimizing fairing usage without compromising control performance, while also promoting layout continuity. Numerical results show that: (1) the optimized spanwise layout reduces the cumulative fairing length by 33.7–41.7% versus full-span installation, with equivalent suppression; (2) the integrated multi-mode layout avoids the overlapping and discontinuous patches inherent in naive superposition of single-mode solutions; and (3) the backtracking greedy algorithm ensures rapid convergence and monotonic feasibility, whereas particle swarm optimization achieves better global exploration at the expense of occasional spatial fragmentation.
{"title":"Intelligent optimization of multi-mode spanwise layouts for vortex-induced vibration suppression in bridges","authors":"Han-yun Liu, Hao-ye Shuai, Na Mao, Li-dong Wang, Yan Han, Peng Hu","doi":"10.1016/j.compstruc.2026.108145","DOIUrl":"10.1016/j.compstruc.2026.108145","url":null,"abstract":"<div><div>This paper presents an intelligent optimization framework for multi-mode spanwise layout optimization of split-type wind fairings on long-span bridges to suppress vortex-induced vibration efficiently and economically. The formulation of spanwise layout optimization minimizes both total fairing length and spatial discontinuities while ensuring control effects across all critical structural modes. The multi-mode control criterion is derived from the condition that the critical total damping ratio of the bridge system equals zero. To satisfy this constraint, a modal energy control ratio is defined for each mode and regulated during optimization. Aerodynamic parameters are identified from segmental wind-tunnel tests on a cable-stayed bridge. Two intelligent optimization algorithms— backtracking greedy search and particle swarm optimization —are implemented, extending established single-mode strategies to the multi-mode context. The objective function prioritizes minimizing fairing usage without compromising control performance, while also promoting layout continuity. Numerical results show that: (1) the optimized spanwise layout reduces the cumulative fairing length by 33.7–41.7% versus full-span installation, with equivalent suppression; (2) the integrated multi-mode layout avoids the overlapping and discontinuous patches inherent in naive superposition of single-mode solutions; and (3) the backtracking greedy algorithm ensures rapid convergence and monotonic feasibility, whereas particle swarm optimization achieves better global exploration at the expense of occasional spatial fragmentation.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108145"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192777","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}
Pub Date : 2026-03-01Epub Date: 2026-02-12DOI: 10.1016/j.compstruc.2026.108144
Anand Kumar , P. Ravi Prakash , Mhd.Anwar Orabi
Structural response forecasting and early warnings during fire events are crucial for enhancing structural safety and supporting effective fire rescue operations. This study proposes an integrated finite element (FE)-based machine learning (ML) framework for forecasting structural responses and establishing an early warning system (EWS) for reinforced concrete (RC) frame structures subjected to fire. A Long Short-Term Memory (LSTM) network is trained using a comprehensive FE simulation dataset generated through a macro-modeling strategy in the GiD–OpenSees interface, with stochastic input parameters to account for uncertainties in fire exposure, material properties, and applied loading. The framework is demonstrated on a three-story, three-bay RC frame, where structural displacements and reinforcement temperatures are forecasted using limited inputs consisting of compartment gas temperatures and joint displacements at peripheral structural locations, over an initial time window. The trained ML model shows high predictive accuracy, with mean absolute error ratios below 5% and coefficient of determination () . An EWS configured from the forecasted response achieves an 85% recall efficiency relative to FE-based failure predictions. The findings highlight the potential of FE-informed ML models to enable structural response forecasting and graded collapse warnings, thereby providing a decision-support framework for fire rescue operations.
{"title":"ML-based structural response forecasting and early warning system for RC structures under fire conditions","authors":"Anand Kumar , P. Ravi Prakash , Mhd.Anwar Orabi","doi":"10.1016/j.compstruc.2026.108144","DOIUrl":"10.1016/j.compstruc.2026.108144","url":null,"abstract":"<div><div>Structural response forecasting and early warnings during fire events are crucial for enhancing structural safety and supporting effective fire rescue operations. This study proposes an integrated finite element (FE)-based machine learning (ML) framework for forecasting structural responses and establishing an early warning system (EWS) for reinforced concrete (RC) frame structures subjected to fire. A Long Short-Term Memory (LSTM) network is trained using a comprehensive FE simulation dataset generated through a macro-modeling strategy in the GiD–OpenSees interface, with stochastic input parameters to account for uncertainties in fire exposure, material properties, and applied loading. The framework is demonstrated on a three-story, three-bay RC frame, where structural displacements and reinforcement temperatures are forecasted using limited inputs consisting of compartment gas temperatures and joint displacements at peripheral structural locations, over an initial time window. The trained ML model shows high predictive accuracy, with mean absolute error ratios below 5% and coefficient of determination (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>) <span><math><mo>≥</mo><mn>0.95</mn></math></span>. An EWS configured from the forecasted response achieves an 85% recall efficiency relative to FE-based failure predictions. The findings highlight the potential of FE-informed ML models to enable structural response forecasting and graded collapse warnings, thereby providing a decision-support framework for fire rescue operations.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108144"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161069","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}
Pub Date : 2026-03-01Epub Date: 2026-02-21DOI: 10.1016/j.compstruc.2026.108159
F. Maksimov , A. Contento , B. Briseghella , P. Cacciola
This paper investigates the nonlinear soil–structure interaction (SSI) of structures protected by a Geotechnical Seismic Isolation (GSI) system using a novel two-stage methodology relying on a DEM-based Preisach formalism. In the first stage, the Distinct Element Method (DEM) is employed to evaluate the key mechanical properties governing foundation–soil interaction and to capture their inherent variability resulting from random particle distributions. In the second stage, the soil’s hysteretic behaviour is modelled using the Preisach formalism, with nonlinear springs and dashpots calibrated using the DEM results. The main novelty of the paper is the derivation of the reduced order SSI model from DEM simulations, which, in turn, require only soil data for calibration. Moreover, the proposed framework enables a comprehensive evaluation of GSI performance through extensive nonlinear numerical simulations that explicitly account for soil variability. A Monte Carlo simulation study is conducted to assess the probabilistic response of an idealized benchmark structure. Comparative analyses between SSI scenarios involving either a natural soil composed of a homogeneous gravel layer or a composite soil profile incorporating a rubber–soil mixture (RSM) demonstrate the flexibility of the proposed method and highlight the influence of RSM on the structural response.
{"title":"Nonlinear soil-structure interaction in geotechnical seismic isolation: A two-stage DEM-Preisach formalism framework","authors":"F. Maksimov , A. Contento , B. Briseghella , P. Cacciola","doi":"10.1016/j.compstruc.2026.108159","DOIUrl":"10.1016/j.compstruc.2026.108159","url":null,"abstract":"<div><div>This paper investigates the nonlinear soil–structure interaction (SSI) of structures protected by a Geotechnical Seismic Isolation (GSI) system using a novel two-stage methodology relying on a DEM-based Preisach formalism. In the first stage, the Distinct Element Method (DEM) is employed to evaluate the key mechanical properties governing foundation–soil interaction and to capture their inherent variability resulting from random particle distributions. In the second stage, the soil’s hysteretic behaviour is modelled using the Preisach formalism, with nonlinear springs and dashpots calibrated using the DEM results. The main novelty of the paper is the derivation of the reduced order SSI model from DEM simulations, which, in turn, require only soil data for calibration. Moreover, the proposed framework enables a comprehensive evaluation of GSI performance through extensive nonlinear numerical simulations that explicitly account for soil variability. A Monte Carlo simulation study is conducted to assess the probabilistic response of an idealized benchmark structure. Comparative analyses between SSI scenarios involving either a natural soil composed of a homogeneous gravel layer or a composite soil profile incorporating a rubber–soil mixture (RSM) demonstrate the flexibility of the proposed method and highlight the influence of RSM on the structural response.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108159"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777915","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}
Pub Date : 2026-03-01Epub Date: 2026-02-18DOI: 10.1016/j.compstruc.2026.108156
Gabriel Edefors , Fredrik Larsson , Karin Lundgren
Accurate modeling of structures exhibiting nonlinear response due to progressive damage, such as cracking, remains a major challenge, as resolving the subscale leads to computationally intensive simulations. To address this, we propose an effective constitutive damage model formulated directly at the sectional level. By expressing the response in terms of generalized sectional quantities, the model eliminates the need for through-thickness integration and evaluation of local material behavior, improving computational efficiency. The formulation is thermodynamically consistent, employs global damage variables in the cross-section, and accounts for the coupling between normal force and bending moment. Calibration and validation are performed against representative volume element-based simulations of textile-reinforced concrete that resolve yarn–matrix slip and matrix softening. Despite its simplicity, the model accurately reproduces axial force and bending moment responses under non-proportional strain and curvature histories. Compared with a fully resolved simulation of a one-way textile-reinforced concrete slab, the model achieves a two-order-of-magnitude reduction in computational cost, with an error below 5 %. The framework captures nonlinear behavior arising from stiffness degradation, making it suitable for textile-reinforced concrete structures in which the structural response is governed by concrete cracking and crushing, as well as bond-degradation. It is, however, also applicable to other beam-like structures exhibiting damage-dominated behavior.
{"title":"A damage-based sectional constitutive model for beams: Application to one-way textile-reinforced concrete slabs","authors":"Gabriel Edefors , Fredrik Larsson , Karin Lundgren","doi":"10.1016/j.compstruc.2026.108156","DOIUrl":"10.1016/j.compstruc.2026.108156","url":null,"abstract":"<div><div>Accurate modeling of structures exhibiting nonlinear response due to progressive damage, such as cracking, remains a major challenge, as resolving the subscale leads to computationally intensive simulations. To address this, we propose an effective constitutive damage model formulated directly at the sectional level. By expressing the response in terms of generalized sectional quantities, the model eliminates the need for through-thickness integration and evaluation of local material behavior, improving computational efficiency. The formulation is thermodynamically consistent, employs global damage variables in the cross-section, and accounts for the coupling between normal force and bending moment. Calibration and validation are performed against representative volume element-based simulations of textile-reinforced concrete that resolve yarn–matrix slip and matrix softening. Despite its simplicity, the model accurately reproduces axial force and bending moment responses under non-proportional strain and curvature histories. Compared with a fully resolved simulation of a one-way textile-reinforced concrete slab, the model achieves a two-order-of-magnitude reduction in computational cost, with an error below 5 %. The framework captures nonlinear behavior arising from stiffness degradation, making it suitable for textile-reinforced concrete structures in which the structural response is governed by concrete cracking and crushing, as well as bond-degradation. It is, however, also applicable to other beam-like structures exhibiting damage-dominated behavior.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108156"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778333","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}
Pub Date : 2026-03-01Epub Date: 2026-02-09DOI: 10.1016/j.compstruc.2026.108143
Zheng Zhou , Paolo Gardoni , Guohai Chen , Dixiong Yang
Although the inerter-based vibration absorbers system has proven effective for structural vibration control, their performance under stochastic near-fault ground motions needs further investigation. However, existing random vibration analysis methods present limitations in simultaneously determining stochastic responses and dynamic reliabilities of large-scale structures accurately and efficiently. This paper proposes a versatile and efficient framework via direct probability integral method to predict stochastic seismic responses and estimate the first-passage reliabilities of the high-rise frame-shear wall structure with tuned viscous mass dampers as inerter-based vibration absorbers. Firstly, two new stochastic spectrum-matched near-fault pulse-like ground motion models are constructed, which can separately synthesize the ground motions with fling-step and forward-directivity pulses. Then, a finite element model of high-rise frame-shear wall structure with tuned viscous mass dampers is developed, and the corresponding building structure with viscous dampers and the prototype structure without dampers are established for comparison. Subsequently, direct probability integral method with iterative sequence sampling strategy is suggested to accurately calculate the means and standard deviations of stochastic seismic responses and efficiently evaluate dynamic reliabilities of these three structures under two types of near-fault stochastic ground motions. Results indicate that the fling-step pulses significantly amplify stochastic acceleration responses, consequently causing severe damage to non-structural components, while the forward-directivity pulses substantially increase stochastic inter-story drifts, resulting in significant damage to structural components. The tuned viscous mass dampers can remarkably reduce stochastic seismic inter-story drifts and accelerations and improve dynamic reliability, with its energy dissipation performance superior to conventional viscous dampers.
{"title":"Stochastic dynamic analysis of high-rise frame-shear wall structure with tuned viscous mass dampers under spectrum-matched near-fault ground motions","authors":"Zheng Zhou , Paolo Gardoni , Guohai Chen , Dixiong Yang","doi":"10.1016/j.compstruc.2026.108143","DOIUrl":"10.1016/j.compstruc.2026.108143","url":null,"abstract":"<div><div>Although the inerter-based vibration absorbers system has proven effective for structural vibration control, their performance under stochastic near-fault ground motions needs further investigation. However, existing random vibration analysis methods present limitations in simultaneously determining stochastic responses and dynamic reliabilities of large-scale structures accurately and efficiently. This paper proposes a versatile and efficient framework via direct probability integral method to predict stochastic seismic responses and estimate the first-passage reliabilities of the high-rise frame-shear wall structure with tuned viscous mass dampers as inerter-based vibration absorbers. Firstly, two new stochastic spectrum-matched near-fault pulse-like ground motion models are constructed, which can separately synthesize the ground motions with fling-step and forward-directivity pulses. Then, a finite element model of high-rise frame-shear wall structure with tuned viscous mass dampers is developed, and the corresponding building structure with viscous dampers and the prototype structure without dampers are established for comparison. Subsequently, direct probability integral method with iterative sequence sampling strategy is suggested to accurately calculate the means and standard deviations of stochastic seismic responses and efficiently evaluate dynamic reliabilities of these three structures under two types of near-fault stochastic ground motions. Results indicate that the fling-step pulses significantly amplify stochastic acceleration responses, consequently causing severe damage to non-structural components, while the forward-directivity pulses substantially increase stochastic inter-story drifts, resulting in significant damage to structural components. The tuned viscous mass dampers can remarkably reduce stochastic seismic inter-story drifts and accelerations and improve dynamic reliability, with its energy dissipation performance superior to conventional viscous dampers.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108143"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135627","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}
Pub Date : 2026-03-01Epub Date: 2026-02-25DOI: 10.1016/j.compstruc.2026.108164
Ioannis P. Mitseas, Omar Danisworo
This paper presents an efficient semi-analytical methodology for quantifying the capsizing risk and seakeeping performance of ships undergoing nonlinear rolling motions under realistic, non-white sea-wave excitations. The dynamic response is captured through a comprehensive and physically consistent nonlinear formulation that incorporates both softening and hardening restoring moment characteristics, nonlinear hydrodynamic damping mechanisms, and evolutionary stochastic wave loads representative of complex maritime environments. By leveraging a refined blend of stochastic averaging and statistical linearization techniques, the study yields computationally efficient, time-dependent seakeeping probability estimates, rigorously accounting for the critical behaviors of both bounded and unbounded ship roll motions, including those associated with negative stiffness regions, through an appropriately tailored, non-stationary response amplitude probability density function (PDF). A notable advancement of the proposed framework lies in its robust capability to address stochastic sea-wave excitations with time-varying intensity and frequency content, thereby accurately reflecting the evolving nature of real-world open-sea environments. Numerical analyses across a range of case studies, validated against benchmark Monte Carlo simulations, demonstrate the accuracy and efficiency of the methodology, underscoring its promise as a practical performance-based tool for evaluating vessel stability and seakeeping under dynamic and uncertain maritime operational scenarios.
{"title":"Stochastic seakeeping analysis of nonlinear ship rolling dynamics under non-stationary and irregular sea states","authors":"Ioannis P. Mitseas, Omar Danisworo","doi":"10.1016/j.compstruc.2026.108164","DOIUrl":"10.1016/j.compstruc.2026.108164","url":null,"abstract":"<div><div>This paper presents an efficient semi-analytical methodology for quantifying the capsizing risk and seakeeping performance of ships undergoing nonlinear rolling motions under realistic, non-white sea-wave excitations. The dynamic response is captured through a comprehensive and physically consistent nonlinear formulation that incorporates both softening and hardening restoring moment characteristics, nonlinear hydrodynamic damping mechanisms, and evolutionary stochastic wave loads representative of complex maritime environments. By leveraging a refined blend of stochastic averaging and statistical linearization techniques, the study yields computationally efficient, time-dependent seakeeping probability estimates, rigorously accounting for the critical behaviors of both bounded and unbounded ship roll motions, including those associated with negative stiffness regions, through an appropriately tailored, non-stationary response amplitude probability density function (PDF). A notable advancement of the proposed framework lies in its robust capability to address stochastic sea-wave excitations with time-varying intensity and frequency content, thereby accurately reflecting the evolving nature of real-world open-sea environments. Numerical analyses across a range of case studies, validated against benchmark Monte Carlo simulations, demonstrate the accuracy and efficiency of the methodology, underscoring its promise as a practical performance-based tool for evaluating vessel stability and seakeeping under dynamic and uncertain maritime operational scenarios.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108164"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147279846","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}
Pub Date : 2026-03-01Epub Date: 2026-02-21DOI: 10.1016/j.compstruc.2026.108158
Guangquan Yu , Ning Li , Cheng Chen , Xiaohang Zhang , Xiaoshu Gao
Surrogate models significantly reduce computational costs by minimizing calls to expensive simulations, enabling efficient reliability analysis and uncertainty quantification. However, traditional surrogate modeling often overlooks transferable knowledge from related tasks, requiring models to be built from scratch. To overcome this limitation, we propose a meta-learning-based adaptive sampling framework for global surrogate modeling that integrates knowledge via a learning-to-learn approach and autonomously selects informative samples for rapid task adaptation. The framework employs meta-learning Gaussian processes (MLGP) to transfer knowledge across tasks during meta-training, while sensitive subdomains are detected using Voronoi partitioning combined with cross-validation error. Within the most sensitive subdomain, variance-guided adaptive sampling is then conducted to further improve convergence. Three numerical case studies, illustrate how varying the number of meta-tasks and samples per task affects prediction accuracy. Applications to composite beams with varying reinforcement parameters and offshore wind structures under different load conditions further demonstrate the framework’s effectiveness in practical engineering contexts. These highlight the framework’s strong potential for scalable, knowledge-efficient surrogate modeling in complex engineering systems.
{"title":"Meta-learning Gaussian processes for engineering surrogate modeling with cross-validation- and variance-guided adaptive sampling","authors":"Guangquan Yu , Ning Li , Cheng Chen , Xiaohang Zhang , Xiaoshu Gao","doi":"10.1016/j.compstruc.2026.108158","DOIUrl":"10.1016/j.compstruc.2026.108158","url":null,"abstract":"<div><div>Surrogate models significantly reduce computational costs by minimizing calls to expensive simulations, enabling efficient reliability analysis and uncertainty quantification. However, traditional surrogate modeling often overlooks transferable knowledge from related tasks, requiring models to be built from scratch. To overcome this limitation, we propose a meta-learning-based adaptive sampling framework for global surrogate modeling that integrates knowledge via a learning-to-learn approach and autonomously selects informative samples for rapid task adaptation. The framework employs meta-learning Gaussian processes (MLGP) to transfer knowledge across tasks during meta-training, while sensitive subdomains are detected using Voronoi partitioning combined with cross-validation error. Within the most sensitive subdomain, variance-guided adaptive sampling is then conducted to further improve convergence. Three numerical case studies, illustrate how varying the number of meta-tasks and samples per task affects prediction accuracy. Applications to composite beams with varying reinforcement parameters and offshore wind structures under different load conditions further demonstrate the framework’s effectiveness in practical engineering contexts. These highlight the framework’s strong potential for scalable, knowledge-efficient surrogate modeling in complex engineering systems.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108158"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777916","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}
Pub Date : 2026-03-01Epub Date: 2026-02-20DOI: 10.1016/j.compstruc.2026.108155
Leo Guo , Hirak Kansara , Siamak F. Khosroshahi , GuoQi Zhang , Wei Tan
Finite element (FE) simulations of structures and materials are becoming increasingly accurate, but also more computationally expensive as a collateral result. This development occurs in parallel with a growing demand for data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. The mesh width of an FE model can be exploited to evaluate an objective at a lower or higher fidelity (cost & accuracy) level, which is the domain of multi-fidelity BO (MFBO) applications. However, BO and MFBO are usually not directly compared in the literature. Moreover, sampling quality and assessing design parameter sensitivity are often underrepresented parts of data-driven design. This paper combines global sensitivity analysis and (MF) BO into a novel, efficient Bayesian data-driven framework. We compare the performance of BO with that of MFBO by maximizing the energy absorption (EA) problem of spinodoid cellular structures. The findings show that similar or better designs are suggested by MFBO with 16% fewer expensive objective evaluations compared to BO when maximizing the EA. The results, which are made open-source, serve to support the utility of multi-fidelity techniques across expensive data-driven design problems.
{"title":"Multi-fidelity Bayesian data-driven design of energy absorbing spinodoid cellular structures","authors":"Leo Guo , Hirak Kansara , Siamak F. Khosroshahi , GuoQi Zhang , Wei Tan","doi":"10.1016/j.compstruc.2026.108155","DOIUrl":"10.1016/j.compstruc.2026.108155","url":null,"abstract":"<div><div>Finite element (FE) simulations of structures and materials are becoming increasingly accurate, but also more computationally expensive as a collateral result. This development occurs in parallel with a growing demand for data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. The mesh width of an FE model can be exploited to evaluate an objective at a lower or higher fidelity (cost & accuracy) level, which is the domain of multi-fidelity BO (MFBO) applications. However, BO and MFBO are usually not directly compared in the literature. Moreover, sampling quality and assessing design parameter sensitivity are often underrepresented parts of data-driven design. This paper combines global sensitivity analysis and (MF) BO into a novel, efficient Bayesian data-driven framework. We compare the performance of BO with that of MFBO by maximizing the energy absorption (EA) problem of spinodoid cellular structures. The findings show that similar or better designs are suggested by MFBO with 16% fewer expensive objective evaluations compared to BO when maximizing the EA. The results, which are made open-source, serve to support the utility of multi-fidelity techniques across expensive data-driven design problems.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108155"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146777917","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}
Pub Date : 2026-03-01Epub Date: 2026-02-19DOI: 10.1016/j.compstruc.2026.108157
Nikhil Mahar , Subhamoy Sen , Laurent Mevel
System identification (SI) is essential for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex systems due to modeling uncertainties and the limited availability of accurate physical models. In contrast, data-driven approaches are computationally efficient but typically lack physical interpretability. Physics-informed neural networks (PINNs) have recently emerged as a promising alternative by combining data with physical laws. However, conventional PINNs are computationally expensive for parameter identification due to collocation-based physics enforcement and multi-objective loss optimization. To overcome these challenges, this study proposes an implicit Runge-Kutta physics-informed neural network based on the Radau IIA discretization scheme, termed as Radau IIA PINN. In the proposed framework, physical laws are embedded directly into the architecture of a recurrent neural network through physics-based time integration, eliminating the need for collocation points and complex regressor construction. A comprehensive comparison with state-of-the-art approaches, including Parallel PINNs, Kalman Filters, Physics-Informed Long Short Term Memory network, and fourth-order Runge–Kutta PINN, demonstrates superior robustness, numerical stability, and accuracy under sparse and noisy measurements. Numerical simulations on various structural systems further confirm faster convergence and reliable identification of localized structural deterioration, highlighting the method’s potential for practical system identification.
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