Pub Date : 2026-03-23DOI: 10.1016/j.compstruc.2026.108201
Yazhou Wang, Dean Maxam, Kumar Tamma, Nikolaus Adams
{"title":"On the comparison of spectral accuracy between single-solve and sub-step time integration algorithms with controllable dissipation parameter","authors":"Yazhou Wang, Dean Maxam, Kumar Tamma, Nikolaus Adams","doi":"10.1016/j.compstruc.2026.108201","DOIUrl":"https://doi.org/10.1016/j.compstruc.2026.108201","url":null,"abstract":"","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"95 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495822","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 : 2026-03-22DOI: 10.1016/j.compstruc.2026.108206
Pierangelo Masarati, Claudio Caccia, Marco Morandini
{"title":"Corrigendum to “Substructuring-based accurate beam section characterization from finite element analysis” [Comput. Struct. 311 (2025) 107720]","authors":"Pierangelo Masarati, Claudio Caccia, Marco Morandini","doi":"10.1016/j.compstruc.2026.108206","DOIUrl":"https://doi.org/10.1016/j.compstruc.2026.108206","url":null,"abstract":"","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"6 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495823","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 : 2026-03-19DOI: 10.1016/j.compstruc.2026.108199
Miaoyu Xu, Shenglan Ma, Chen Wu, Shaofei Jiang
{"title":"Robust finite element model updating framework for cable-net structures during construction using fusion neural network with convolutional neural networks and self-attention mechanisms","authors":"Miaoyu Xu, Shenglan Ma, Chen Wu, Shaofei Jiang","doi":"10.1016/j.compstruc.2026.108199","DOIUrl":"https://doi.org/10.1016/j.compstruc.2026.108199","url":null,"abstract":"","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"92 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495837","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 : 2026-03-16DOI: 10.1016/j.compstruc.2026.108197
Sergiy Fialko
An approach for analysis buildings and structures under the action of extreme loads based on the sequential consideration of progressive destruction, and then, numerical modeling of the leveling and repair stages, is proposed. Extreme impacts are a strong earthquake, an explosion of household gas, sabotage, a drone or artillery shell’s strike, etc. Each stage of the finite element analysis is performed using a nonlinear dynamic analysis, taking into account the plastic properties of the material, elements of damage mechanics, when the deformations of structural elements exceed the maximum allowable ones, as well as the geometric nonlinearity based on updated Lagrangian formulation. A new method for removing finite elements is proposed, which makes it possible to remove at specified points in time not only columns or beams, but also finite elements of arbitrary types, such as plane shell finite elements, applied for modeling load-bearing walls and staircase-elevator blocks. The purpose of the leveling stage is to bring the highly deformed structure closer to its original configuration. This is achieved by imposing additional supports and constraints and setting their imposed displacements. The repair stage consists of adding new finite elements to the calculation model and removing previously imposed temporary supports and constraints.
{"title":"Finite element modeling of collapse, leveling and repair of multi-storey buildings exposed to extreme impacts","authors":"Sergiy Fialko","doi":"10.1016/j.compstruc.2026.108197","DOIUrl":"https://doi.org/10.1016/j.compstruc.2026.108197","url":null,"abstract":"An approach for analysis buildings and structures under the action of extreme loads based on the sequential consideration of progressive destruction, and then, numerical modeling of the leveling and repair stages, is proposed. Extreme impacts are a strong earthquake, an explosion of household gas, sabotage, a drone or artillery shell’s strike, etc. Each stage of the finite element analysis is performed using a nonlinear dynamic analysis, taking into account the plastic properties of the material, elements of damage mechanics, when the deformations of structural elements exceed the maximum allowable ones, as well as the geometric nonlinearity based on updated Lagrangian formulation. A new method for removing finite elements is proposed, which makes it possible to remove at specified points in time not only columns or beams, but also finite elements of arbitrary types, such as plane shell finite elements, applied for modeling load-bearing walls and staircase-elevator blocks. The purpose of the leveling stage is to bring the highly deformed structure closer to its original configuration. This is achieved by imposing additional supports and constraints and setting their imposed displacements. The repair stage consists of adding new finite elements to the calculation model and removing previously imposed temporary supports and constraints.","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"52 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147465750","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: 2026-02-16DOI: 10.1016/j.compstruc.2026.108141
Muhammad Uzair , Saullo G.P. Castro , José Humberto S. Almeida Jr.
This study presents an imperfection-tolerant, surrogate-assisted framework for the multi-objective optimisation of variable-stiffness (VS) composite cylinders that explicitly incorporates experimentally measured geometric imperfections. Principal component analysis (PCA) is applied to extract dominant imperfection modes from experimental data, and Latin hypercube sampling (LHS) is used to generate statistically consistent synthetic fields, which are subsequently mapped onto nonlinear finite element (FE) models. Linear buckling and geometrically nonlinear collapse analyses are performed under axial compression to determine the ideal and actual load-carrying capacities, from which the knockdown factor (KDF), quantifying imperfection sensitivity, is derived. Gaussian Process Regression (GPR) surrogates are trained to predict the mass and collapse loads of perfect and imperfect geometries with high cross-validated accuracy, while KDF is computed as their ratio. The framework enables simultaneous optimisation of three objectives: mass minimisation, collapse-load maximisation, and KDF maximisation by using Bayesian Optimisation (BO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) independently. Results demonstrate that integrating experimentally informed imperfections with surrogate-based optimisation captures the key physical trends governing buckling and imperfection sensitivity, while achieving substantial computational savings relative to direct nonlinear analyses, and that both optimisers yield consistent Pareto fronts featuring smooth, manufacturable fibre trajectories that balance lightweight efficiency, strength, and robustness.
{"title":"Multi-objective optimisation of variable-stiffness composite cylinders with geometric imperfections: minimising mass while maximising buckling capacity and knockdown factor","authors":"Muhammad Uzair , Saullo G.P. Castro , José Humberto S. Almeida Jr.","doi":"10.1016/j.compstruc.2026.108141","DOIUrl":"10.1016/j.compstruc.2026.108141","url":null,"abstract":"<div><div>This study presents an imperfection-tolerant, surrogate-assisted framework for the multi-objective optimisation of variable-stiffness (VS) composite cylinders that explicitly incorporates experimentally measured geometric imperfections. Principal component analysis (PCA) is applied to extract dominant imperfection modes from experimental data, and Latin hypercube sampling (LHS) is used to generate statistically consistent synthetic fields, which are subsequently mapped onto nonlinear finite element (FE) models. Linear buckling and geometrically nonlinear collapse analyses are performed under axial compression to determine the ideal and actual load-carrying capacities, from which the knockdown factor (KDF), quantifying imperfection sensitivity, is derived. Gaussian Process Regression (GPR) surrogates are trained to predict the mass and collapse loads of perfect and imperfect geometries with high cross-validated accuracy, while KDF is computed as their ratio. The framework enables simultaneous optimisation of three objectives: mass minimisation, collapse-load maximisation, and KDF maximisation by using Bayesian Optimisation (BO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) independently. Results demonstrate that integrating experimentally informed imperfections with surrogate-based optimisation captures the key physical trends governing buckling and imperfection sensitivity, while achieving substantial computational savings relative to direct nonlinear analyses, and that both optimisers yield consistent Pareto fronts featuring smooth, manufacturable fibre trajectories that balance lightweight efficiency, strength, and robustness.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108141"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147423033","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}
Peridynamics is a powerful tool for modelling crack propagation, but its high computational cost limits large-scale applications. To overcome this issue, coupling peridynamics with efficient classical approaches such as the finite strip method can significantly reduce the computational costs. In this study, peridynamics is coupled, for the first time, with the finite strip method to analyse the out-of-plane deformation of plates. Several static analyses are carried out, and the results are compared with those obtained from the finite element method (ABAQUS). In addition, the influence of key discretization parameters on the accuracy and efficiency is investigated. Finally, a case study is carried out to demonstrate the capability of the proposed model in simulating crack propagation.
{"title":"Out-of-plane deformation analysis of plates using a coupled peridynamic-finite strip method","authors":"Zahra Shafiei , Saeid Sarrami , Mojtaba Azhari , Ugo Galvanetto , Mirco Zaccariotto","doi":"10.1016/j.compstruc.2026.108160","DOIUrl":"10.1016/j.compstruc.2026.108160","url":null,"abstract":"<div><div>Peridynamics is a powerful tool for modelling crack propagation, but its high computational cost limits large-scale applications. To overcome this issue, coupling peridynamics with efficient classical approaches such as the finite strip method can significantly reduce the computational costs. In this study, peridynamics is coupled, for the first time, with the finite strip method to analyse the out-of-plane deformation of plates. Several static analyses are carried out, and the results are compared with those obtained from the finite element method (ABAQUS). In addition, the influence of key discretization parameters on the accuracy and efficiency is investigated. Finally, a case study is carried out to demonstrate the capability of the proposed model in simulating crack propagation.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108160"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278430","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}
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are reformulated into dimensionless parameters that capture underlying physical principles. Predicted geometries are compactly represented using Fourier descriptors, which enforce smooth, closed, and symmetric profiles while reducing the prediction task to a small set of coefficients. The training dataset was synthetically generated using a well-established Particle Finite Element Method (PFEM) model of 3DCP, overcoming the scarcity of experimental data. Validation against diverse numerical and experimental cases shows strong agreement, confirming the machine learning framework’s accuracy and reliability. This opens the way to practical applications, from pre-calibrating print settings and reducing trial-and-error adjustments to optimizing toolpaths for more advanced designs. Looking ahead, coupling the framework with simulations and sensor feedback could enable closed-loop digital twins for 3DCP, driving real-time process optimization, defect detection, and adaptive control of printing parameters.
{"title":"ShapeGen3DCP: A deep learning framework for layer shape prediction in 3D concrete printing","authors":"Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi","doi":"10.1016/j.compstruc.2026.108142","DOIUrl":"10.1016/j.compstruc.2026.108142","url":null,"abstract":"<div><div>This work introduces <em>ShapeGen3DCP</em>, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are reformulated into dimensionless parameters that capture underlying physical principles. Predicted geometries are compactly represented using Fourier descriptors, which enforce smooth, closed, and symmetric profiles while reducing the prediction task to a small set of coefficients. The training dataset was synthetically generated using a well-established Particle Finite Element Method (PFEM) model of 3DCP, overcoming the scarcity of experimental data. Validation against diverse numerical and experimental cases shows strong agreement, confirming the machine learning framework’s accuracy and reliability. This opens the way to practical applications, from pre-calibrating print settings and reducing trial-and-error adjustments to optimizing toolpaths for more advanced designs. Looking ahead, coupling the framework with simulations and sensor feedback could enable closed-loop digital twins for 3DCP, driving real-time process optimization, defect detection, and adaptive control of printing parameters.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"323 ","pages":"Article 108142"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160926","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}
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