Computers & Structures最新文献

{"title":"The progressive ply interpolation model for the optimization design of double-double laminates with gradual thickness tapering","authors":"Pingchu Fang,&nbsp;Tong Gao,&nbsp;Shanyue Gao,&nbsp;Yongbin Huang,&nbsp;Weihong Zhang","doi":"10.1016/j.compstruc.2025.107790","DOIUrl":"10.1016/j.compstruc.2025.107790","url":null,"abstract":"<div><div>Double–double laminates can offer a lightweight design that is easy to taper by varying the number of repeated sub-plies. In this work, an innovative gradual thickness tapering optimization design method for DD laminates has been proposed, enabling the ply-drop design of DD laminates. Specifically, the progressive ply interpolation model has been put forward, simultaneously taking into account the current element thickness and the element thickness within the local search area. This method can effectively prevent the occurrence of empty sub-plies and the simultaneous change of multiple sub-plies in the DD laminate structure, and can also control the taper size. Finally, through three examples, including the aircraft wing in an engineering project, the influencing factors of the optimization results are discussed. It is demonstrated that this method has great potential in enabling the DD laminate structure to achieve a higher stiffness-to-mass ratio.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107790"},"PeriodicalIF":4.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874323","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":"Mesoscale modelling of single-aggregate debonding behaviour in asphalt pavement using the combined finite and discrete element method","authors":"Jiale Lu ,&nbsp;Baofeng Pan ,&nbsp;Jinghui Zhang ,&nbsp;Peng Yin ,&nbsp;Tianling Dong","doi":"10.1016/j.compstruc.2025.107788","DOIUrl":"10.1016/j.compstruc.2025.107788","url":null,"abstract":"<div><div>Aggregate loss from artificial asphalt pavement surfaces widely happens, which causes premature deterioration of roads. However, the debonding mechanism of geographical aggregate is still unrevealed owing to the intricate aggregate morphology and the multiphase structure. To address this challenge, a combined finite and discrete element method (FDEM) was presented to simulate the debonding behaviour in the mesoscale. A mesh-size sensitivity study was first conducted on the asphalt composite to ensure the model self-consistency. Then a realistic particle shape with natural morphology from X-ray micro-computed tomography (XCT) was imported into the numerical model. To explore the role of the aggregate shape on debonding behaviour, systematic simulations were also performed on spherical, ellipsoidal particles for comparison. Results show that the curvature variation could significantly enhance the nesting effect, facilitating stress diffusion. Meanwhile, the further enrichment of irregular curvature variation on real particles results in a more uniform distribution of failure area concerning the aggregate and interface, however, reduces the maximum reaction moment for debonding. The breaking moments for both ellipsoidal and realistic aggregates obey the Generalized extreme value (GEV) distribution. This study helps improve the cognition of aggregate shape effect on debonding behaviour and facilitates the design optimization of the asphalt wearing course.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107788"},"PeriodicalIF":4.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876700","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":"Metamodeling for robust design of energy harvesting devices using polynomial chaos expansion and artificial neural networks","authors":"Paulo Henrique Martins,&nbsp;Ramiro J. Chamorro Coneo,&nbsp;Auteliano Antunes dos Santos Jr","doi":"10.1016/j.compstruc.2025.107785","DOIUrl":"10.1016/j.compstruc.2025.107785","url":null,"abstract":"<div><div>The generation of electrical energy using piezoelectric devices represents a promising alternative due to the high charge density these materials can generate. Cantilever beam devices modeled using finite element methods are commonly used in studies focused on the conversion of mechanical energy into electrical energy. With this, the influence of specific variables and parameters can be analyzed through the Frequency Response Function (FRF) of power output. In the search for optimal solutions, it is important to consider uncertainties in parameters in order to design robust devices. Due to the high computational cost of robustness analysis, particularly when considering the mean and relative dispersion, the generation of computational metamodels becomes interesting for their characteristics. This work aims to develop and evaluate a metamodeling approach for the mentioned FRF using polynomial chaos expansion and artificial neural networks, assessing which method provides better accuracy. After the analysis, a method is chosen to generate the metamodel and multi-objective optimization with algorithm NSGA-II is applied to maximize the mean and minimize the dispersion of FRF. The results demonstrate that metamodels can effectively approximate the outcomes obtained from the original function in scenarios characterized by significant uncertainties, with relatively low computational effort.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107785"},"PeriodicalIF":4.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874319","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":"Deep learning-based post-earthquake structural damage level recognition","authors":"Xiaoying Zhuang ,&nbsp;Than V. Tran ,&nbsp;H. Nguyen-Xuan ,&nbsp;Timon Rabczuk","doi":"10.1016/j.compstruc.2025.107761","DOIUrl":"10.1016/j.compstruc.2025.107761","url":null,"abstract":"<div><div>Rapid assessment of building damage levels has become very important and has received considerable attention in structural engineering. Traditional methods for this work involve manual inspection, which is often tedious and time-consuming. Deep learning technology in computer vision has developed rapidly in recent years and has proven its superiority. This paper aims to develop an efficient approach to recognize quick post-earthquake structural damage levels. First, we develop a feature extraction with seven pre-trained CNN models (Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, NASNetMobile) on a small dataset of 2000 images. The CNN models are then trained by five fold cross-validation. The performance of the models is compared on a testing set, the MobileNet model demonstrated the best classifier performance with an accuracy of 90.89 %. Second, the Bayesian optimization method and the fine-tuning strategy are used to find the optimal hyperparameters of the MobileNet model. The results revealed that the performance of the MobileNet model increased significantly with an accuracy of 96.11 %. Third, Gradient-weighted class activation mapping (Grad-CAM) is used to highlight crucial regions on structural damage images for CNN’s prediction. Finally, the generalizability of the MobileNet model is improved by training it on an extended dataset of 3600 images. The proposed approach demonstrates the feasibility and potential uses of deep learning in image-based structural damage level recognition.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107761"},"PeriodicalIF":4.4,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868517","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":"Generalized reconfigurations and growth mechanics of biological structures considering regular and irregular features: A computational study","authors":"Nasser Firouzi ,&nbsp;Krzysztof Kamil Żur ,&nbsp;Timon Rabczuk ,&nbsp;Xiaoying Zhuang","doi":"10.1016/j.compstruc.2025.107781","DOIUrl":"10.1016/j.compstruc.2025.107781","url":null,"abstract":"<div><div>Many soft biological structures have natural features of viscoelastic and hyperelastic materials. Research focused on the growth biomechanics of these structures is challenging from theoretical and experimental points of view, especially when irregular forms/defects of biological objects should be considered. To this aim, an effort is made in this paper to develop a general nonlinear finite element model for the growth of biological soft structures such as arteries, skin or different tissues. The non-Newtonian fluid is considered for viscoelastic branches. The effect of variation in thickness growth and irregular geometry as well as defects of biostructure is taken into account for the first time. The general nonlinear formulations are obtained for isotropic as well as anisotropic material properties. Furthermore, to resolve evolution equations resulting of internal variables for growth as well as viscoelastic branches, two effective implicit trapezoidal time integration schemes are employed. To study the applicability of the proposed model, the obtained results are compared with results from clinical studies for skin growth, available in the literature. The results demonstrate that the present model enables to capture of the experimental observations with very good accuracy. Additionally, the presented model enables to study of different shapes of biostructure, and variation in thickness growth, including regular and irregular defects, which have never been investigated previously.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107781"},"PeriodicalIF":4.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868514","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 static finite element model updating using the Bayesian method integrating homotopy surrogate model","authors":"Bin Huang ,&nbsp;Ming Sun ,&nbsp;Hui Chen ,&nbsp;Zhifeng Wu","doi":"10.1016/j.compstruc.2025.107769","DOIUrl":"10.1016/j.compstruc.2025.107769","url":null,"abstract":"<div><div>The Bayesian model updating method usually involves tens of thousands of finite element model calculations, which will bring huge computational costs to large structures such as bridges. To reduce the computational costs, this paper develops a highly efficient Bayesian model updating method based on a new static homotopy surrogate model. The new surrogate model is established on the basis of the finite element model using the stochastic homotopy method, which is different from the existing surrogate models that depend on the selected samples. Then by using the hybrid Monte Carlo sampling algorithm integrating the homotopy surrogate model, the static Bayesian model updating of structure is implemented. The numerical example of a plate demonstrates that the established surrogate model has higher accuracy than the polynomial response surface model and Kriging model. Based on the uncertain static test data, the finite element model of a continuous concrete box-girder bridge is efficiently updated using the new method. And the statistics of the displacements in the updated bridge are in good agreement with that of the uncertain measurement data.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107769"},"PeriodicalIF":4.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868516","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 probabilistic semi-explicit model for crack propagation in concrete structures under dynamic loading","authors":"Gustavo Luz Xavier da Costa ,&nbsp;Pierre Rossi ,&nbsp;Mariane Rodrigues Rita ,&nbsp;Magno Teixeira Mota ,&nbsp;Rodolfo Giacomim Mendes de Andrade ,&nbsp;Eduardo de Moraes Rego Fairbairn","doi":"10.1016/j.compstruc.2025.107783","DOIUrl":"10.1016/j.compstruc.2025.107783","url":null,"abstract":"<div><div>In this paper, concrete cracking is investigated in dynamics through finite element modeling. A probabilistic approach is employed to translate the effects of material heterogeneity on tensile strength and fracture energy. Both parameters depend on compressive strength and heterogeneity degree (volumetric ratio between finite element and largest aggregate). Material softening is modeled through damage theory. The actual concrete strengthening effect is modeled by an empirical formulation and similar reasoning is adopted for fracture energy. The apparent strengthening effect is naturally captured when mass and damping are included in the equation of motion. A convergence test is shown, indicating the probabilistic model proposed here is mesh-insensitive, converging in the average sense when both finite element size and load/time increment decrease. Then, experimental data are selected from literature for a wide range of loading rates. The effect of strain rate on the dispersion of crack pattern, load-carrying capacity and load–displacement curve is discussed. The influence of structural damping on the shape of load–displacement curve is also remarked.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107783"},"PeriodicalIF":4.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868515","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":"Transfer learning-based artificial neural networks for hysteresis response prediction of steel braces","authors":"Sepehr Pessiyan,&nbsp;Fardad Mokhtari,&nbsp;Ali Imanpour","doi":"10.1016/j.compstruc.2025.107777","DOIUrl":"10.1016/j.compstruc.2025.107777","url":null,"abstract":"<div><div>This paper proposes a novel data-driven surrogate model for predicting the hysteresis response, i.e., axial force – axial deformation, of steel braces in concentrically braced frames under seismic loading using transfer learning-based artificial neural networks. Transfer learning is utilized to leverage pre-trained baseline long short-term memory networks and transfer its knowledge to the new hysteresis surrogate model. The proposed model is validated using four case studies involving various combinations of input data obtained from laboratory tests and data generated using random earthquake-induced vibration, featuring a wide range of frequency contents, amplitudes, and durations. A pseudo-dynamic analysis is then performed on a steel braced frame system to demonstrate the application of the proposed surrogate model in system-level response evaluation while verifying the performance of the model in real-time seismic simulations. The results obtained from the validation study confirm that the proposed brace hysteresis model can properly estimate the underlying physical relationship between the input displacement and output force using the transfer learning approach. The proposed model offers an efficient method to evaluate the dynamic response of steel braced frames.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107777"},"PeriodicalIF":4.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859630","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":"Simulation of the TNT-based melt-cast explosive charging process using hot mandrel assisted solidification","authors":"Xuezhen Zhai,&nbsp;Yongjia Zhang,&nbsp;Ge Kang,&nbsp;Pengwan Chen","doi":"10.1016/j.compstruc.2025.107780","DOIUrl":"10.1016/j.compstruc.2025.107780","url":null,"abstract":"<div><div>The melt-cast charging process, widely used in warheads for its adaptability, cost efficiency, and automation, requires optimization to minimize defects such as shrinkage cavities and porosity that compromise explosive quality, destructive power, and safety, particularly in large-volume munitions. The hot mandrel technique, by providing localized heating during solidification, helps maintain an open feeding channel, thereby reducing defect formation and improving charge integrity. In this study, the solidification process of a TNT-based melt-cast explosive is investigated using ProCAST combined with an orthogonal test approach, focusing on the hot mandrel charging technique for a warhead. The influence of three primary process parameters—the hot mandrel length, heating time, and temperature—on the solidification process is analyzed. The results demonstrate that, compared to traditional natural solidification, the solidification process with hot mandrel assistance significantly reduces the occurrence of shrinkage cavities and porosity defects, decreases the volume of shrinkage-related flaws, and enhances the overall charge quality. Among the parameters studied, the heating time of the hot mandrel exerts the greatest influence on charge quality, followed by its temperature and length. Prolonging the heating time not only reduces shrinkage defects but also extends the solidification duration. Considering both defect reduction and solidification efficiency, the optimal process conditions within the tested range are as follows: a hot mandrel length of 350 mm, a heating time of 4000 s, and a hot mandrel temperature of 90 <span><math><msup><mspace></mspace><mrow><mo>∘</mo></mrow></msup></math></span>C. This study innovatively develops a numerical simulation approach using ProCAST for hot mandrel-assisted solidification, systematically investigating the effects of three critical parameters on charge quality. The proposed optimization framework balances defect control with production efficiency, providing theoretical guidance for industrial implementation.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107780"},"PeriodicalIF":4.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859631","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 new three-dimensional model of train-track-bridge coupled system based on meshless method and its graph neural network-based surrogate model","authors":"Zhanjun Shao ,&nbsp;Peng Zhang ,&nbsp;Xiaonan Xie ,&nbsp;Zihe Wang ,&nbsp;Xuan Peng ,&nbsp;Zefeng Liu ,&nbsp;Yufei Chen ,&nbsp;Ping Xiang","doi":"10.1016/j.compstruc.2025.107786","DOIUrl":"10.1016/j.compstruc.2025.107786","url":null,"abstract":"<div><div>A model of train–track–bridge coupled system is proposed to study the interactions between structures in greater detail. The new model employs a meshless method to numerically simulate the box girder bridge and track slab. In the dynamic analysis, the system at each time step is abstracted into a graph structure and trained using a graph neural network to develop a surrogate prediction model. The graph neural network node connections in the bridge top plate are determined by the meshless method. Multiple numerical examples demonstrate the differences in structural response between the proposed model and the conventional model and evaluate the performance and self-evolutionary capabilities of the surrogate model. The results indicate that, compared to the proposed model, the conventional model underestimates vertical responses by approximately 17 %–69 % and lateral responses by one to two orders of magnitude. The surrogate model demonstrates good displacement prediction capabilities for the bridge on the training dataset, achieving an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> value as high as 0.99. Furthermore, it exhibits robust prediction and self-evolutionary capabilities on the test dataset under topological changes, with prediction accuracy decreasing by only about 2 %. However, the prediction performance for rail responses is relatively poor, with an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> value as low as 0.29.</div></div>","PeriodicalId":50626,"journal":{"name":"Computers & Structures","volume":"315 ","pages":"Article 107786"},"PeriodicalIF":4.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859632","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}