Advances in Engineering Software最新文献

{"title":"Machine learning guided analysis and rapid design of a 3D-printed bio-inspired structure for energy absorption","authors":"Feng Zhu ,&nbsp;Kael Kinney ,&nbsp;Wenye He ,&nbsp;Zhiqing Cheng","doi":"10.1016/j.advengsoft.2024.103714","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103714","url":null,"abstract":"<div><p>Mantis shrimps employ their telson, or tail plate, to mitigate the impact with hard surfaces, thanks to its unique double-sine shaped microstructures that absorb energy through deformation. Inspired by this natural impact-resistant design, similar lightweight energy absorbers have been developed for applications in transportation systems and personal protective equipment. This study presents a data-driven approach to analyze and optimize these structures subjected to crushing loads. The structure's geometry is defined by three simple parameters based on a sine wave shape function and fabricated using ABS-M30 polymer through 3D printing. Material tests and compression tests under uniaxial loading conditions are conducted to characterize the material properties and structural behavior. Finite element models are created to simulate these tests, and Machine Learning techniques are applied to study the structure's behavior. A total of 100 Design of Computer Experiments are generated by manipulating the design variables, and the Decision Tree method categorizes deformation modes. Intrinsic and response parameters are predicted as functions of the geometric parameters. Using these relationships, a multi-objective optimal design is achieved, enhancing specific energy absorption while reducing peak crush force. The Pareto Front, representing optimal designs for these objectives, is obtained through genetic algorithms. A multi-criteria decision-making algorithm factors in designer preferences to narrow down the optimal design dataset. This study highlights the potential of bio-inspired structures and design methodologies for innovative lightweight protective equipment in transportation systems and human wearables.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103714"},"PeriodicalIF":4.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582588","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":"Physics-informed neural network for nonlinear analysis of cable net structures","authors":"Dai D. Mai ,&nbsp;Tri Diep Bao ,&nbsp;Thanh-Danh Lam ,&nbsp;Hau T. Mai","doi":"10.1016/j.advengsoft.2024.103717","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103717","url":null,"abstract":"<div><p>In this study, a Physics-Informed Neural Network (PINN) framework is extended and applied to predict the geometrically nonlinear responses of pretensioned cable net structures without utilizing any incremental-iterative algorithms as well as Finite Element Analyses (FEAs). Instead of solving nonlinear equations as in existing numerical models, the core idea behind this approach is to employ a Neural Network (NN) that minimizes a loss function. This loss function is designed to guide the learning process of the network based on Total Potential Energy (TPE), pretension forces, and Boundary Conditions (BCs). The NN itself models the displacements given the corresponding coordinates of joints as input data, with trainable parameters including weights and biases that are regarded as design variables. Within this computational framework, these parameters are automatically adjusted through the training process to get the minimum loss function. Once the learning is complete, the nonlinear responses of cable net structures can be easily and quickly obtained. A series of numerical examples is investigated to demonstrate the effectiveness and applicability of the PINN for the geometrically nonlinear analysis of cable net structures. The obtained results indicate that the PINN framework is remarkably simple to use, robust, and yields higher accuracy.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103717"},"PeriodicalIF":4.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582586","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":"Enhancements in image quality and block detection performance for Reinforced Soil-Retaining Walls under various illuminance conditions","authors":"Yong-Soo Ha ,&nbsp;Myounghak Oh ,&nbsp;Minh-Vuong Pham ,&nbsp;Ji-Sung Lee ,&nbsp;Yun-Tae Kim","doi":"10.1016/j.advengsoft.2024.103713","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103713","url":null,"abstract":"<div><p>To ensure continuous monitoring of reinforced soil-retaining walls (RSWs) even under low-illuminance conditions, such as during the night, it is imperative to evaluate the performance of deep learning-based detection. In this study, we constructed a laboratory RSW model and generated a dataset with varying illuminance levels to assess the impact of image enhancement and block detection. Various image enhancement methods were applied to improve image quality and evaluate their effect on deep learning. RGB optimization (RO) was proposed to optimize RGB intensity and compared with gamma correction, histogram equalization, and low-light image enhancement with illumination map estimation. RO demonstrated outstanding image enhancement performance, as evidenced by lightness order error, peak signal-to-noise ratio, and structural similarity index measure, ensuring high image quality. The trained RO model using Mask R-CNN exhibited excellent accuracy, recall, and F1 score, delivering remarkable detection performance under low illuminance conditions, resulting in a 7.44 % improvement in the F1 score. Image enhancement techniques that maintain similarity, such as lightness order error and structural similarity, across varying illuminance conditions contribute to enhancing the block detection performance of Mask R-CNNs.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"195 ","pages":"Article 103713"},"PeriodicalIF":4.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543437","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":"The path-engulfment method for topology optimization of structures","authors":"Jiahui Lin ,&nbsp;Yue Zhou ,&nbsp;Shuo Han ,&nbsp;Yanjun Li ,&nbsp;Zonglai Mo ,&nbsp;Jun Li","doi":"10.1016/j.advengsoft.2024.103715","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103715","url":null,"abstract":"<div><p>To address the challenge of establishing and solving mathematical models for engineering structural optimization, a new topological optimization method that integrates load-transfer path theory with the engulfment algorithm is presented in this paper. The presented method applies the load-transfer path theory to identify the main load-bearing areas of the structure and utilizes the principle of concentrating more materials in relatively high-stress regions and fewer materials in relatively low-stress regions. An engulfment algorithm is introduced to optimize the material distribution. A comparative analysis between the presented and variable-density methods revealed that the path-engulfment method enhances the structural stiffness and strength while reducing its mass, confirming its precision and efficacy in structural optimization. The path-engulfment method was implemented on a truck crane frame, resulting in an optimized structure with increased stiffness and strength and reduced mass compared to the original design. Furthermore, this method eliminates the need for establishing and solving complex mathematical models while addressing issues related to checkerboards and gray-scale elements. A smooth boundary approach was introduced by leveraging the engulfment algorithm, enabling the direct application of the optimized structure for manufacturing purposes, particularly in engineering applications.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103715"},"PeriodicalIF":4.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539302","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":"The crashworthiness prediction and deformation constraint optimization of shrink energy-absorbing structures based on deep learning architecture","authors":"Jiaxing He ,&nbsp;Ping Xu ,&nbsp;Jie Xing ,&nbsp;Shuguang Yao ,&nbsp;Bo Wang ,&nbsp;Xin Zheng","doi":"10.1016/j.advengsoft.2024.103719","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103719","url":null,"abstract":"<div><p>The deformation behavior of shrink energy-absorbing structures is influenced by numerous factors, and improper matching of parameters in the design process can easily lead to buckling instability, or even failure to absorb energy. Existing research methods can only obtain descriptive laws on how structural parameters affect deformation modes, but cannot determine the parameter domain for stable shrink mode, leading to poor prediction and optimization effects. For this purpose, a crashworthiness prediction framework based on deformation image generation and classification network (DIGCNet) was proposed to accurately predict the mean crushing force (MCF) and specific energy absorption (SEA) in the shrink mode domain. An image generator and a classification network were used to establish mapping relationships from structural parameters to deformation modes. The effects of the DIGCNet hyperparameters on prediction accuracy were analyzed. Subsequently, the shrink energy-absorbing structure was optimized under deformation constraint, and compared to the unconstrainted solution. The results show that the DIGCNet can eliminate abnormal deformations and achieve the structural optimization under the parameter domain of the shrink mode.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103719"},"PeriodicalIF":4.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539314","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 approach for identifying vertical temperature gradient in steel-concrete composite beam under solar radiation","authors":"Yonghao Chu ,&nbsp;Yuping Zhang ,&nbsp;Siyang Li ,&nbsp;Yugang Ma ,&nbsp;Shengjiang Yang","doi":"10.1016/j.advengsoft.2024.103695","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103695","url":null,"abstract":"<div><p>The traditional research methods for the temperature field of bridge under solar radiation suffer from issues such as high workload and high costs. The temperature field of steel-concrete composite beam (SCCB) is studied in this paper using the ANSYS finite element software and MATLAB software. Firstly, a finite element temperature field model of SCCB is established based on measured meteorological data. Furthermore, the accuracy of the finite element temperature field model of SCCB is validated by collecting a small amount of temperature measurement data. The temperature sample database of SCCB was expanded based on this. Finally, a large amount of historical meteorological data was collected. The ANSYS software and Genetic Algorithm Back Propagation (GA-BP) hybrid model were used for calculation, and the representative temperature differences <em>T</em>_d1 and <em>T</em>_d2 of SCCB were obtained separately. The measured values are in good agreement with the finite element analysis results, showing consistent trends over time with a maximum difference not exceeding 1.6 °C. The GA-BP hybrid model proposed in this study, characterized by ‘structural features, temporal features, environmental features—node temperatures’, exhibits a high degree of nonlinear mapping capability. It has been demonstrated that the GA-BP hybrid model also possesses a high level of accuracy through verification. The SCCBs’ maximum vertical positive temperature differences (<em>T</em>_v), computed using ANSYS software and the GA-BP hybrid model, follow Generalized Extreme Value (GEV) distributions with parameters (-0.2722, 12.8715, 1.4105) and (-0.2855, 12.813, 1.3714), respectively. The representative values (<em>T</em>_d) of the maximum vertical positive temperature differences of SCCB, calculated by ANSYS software and the GA-BP hybrid model, are 17.613 °C (<em>T</em>_d1) and 17.2 °C (<em>T</em>_d2), respectively. The proposed temperature field calculation model for SCCB is based on meteorological parameters and the GA-BP hybrid model. It can accurately calculate the temperature field of SCCB in Guangdong region and improve computational efficiency.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103695"},"PeriodicalIF":4.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141539315","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 of flexible neighbors lists in Smoothed Particle Hydrodynamics on GPU","authors":"Giuseppe Bilotta ,&nbsp;Vito Zago ,&nbsp;Alexis Hérault ,&nbsp;Annalisa Cappello ,&nbsp;Gaetana Ganci ,&nbsp;Hendrik D. van Ettinger ,&nbsp;Robert A. Dalrymple","doi":"10.1016/j.advengsoft.2024.103711","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103711","url":null,"abstract":"<div><p>Recent refactoring of the GPUSPH codebase have uncovered some of the limitations of the official CUDA compiler (<span>nvcc</span>) offered by NVIDIA when dealing with some C++ constructs, which has shed some new light on the relative importance of the neighbors list construction and traversal in SPH codes, presenting new possibility of optimization with surprising performance gains. We present our solution for high-performance neighbors list construction and traversal, and show that a <span><math><mrow><mn>4</mn><mo>×</mo></mrow></math></span> speedup can be achieved in industrial applications.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"196 ","pages":"Article 103711"},"PeriodicalIF":4.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0965997824001182/pdfft?md5=50c20d5ae7d4d324220dfbb35b9727fe&pid=1-s2.0-S0965997824001182-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141484902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mesh objective characteristic element length for higher-order finite beam elements","authors":"J. Shen ,&nbsp;M.R.T. Arruda ,&nbsp;A. Pagani ,&nbsp;M. Petrolo","doi":"10.1016/j.advengsoft.2024.103709","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103709","url":null,"abstract":"<div><p>The use of fracture energy regularization techniques can effectively mitigate the mesh dependency of numerical solutions caused by the strain softening behavior of quasi-brittle materials. However, the successful regularization depends on the correct estimation of the crack bandwidth in Finite Element solutions. This paper aims to present an enhanced crack band formulation to overcome the strain localization instability especially for the higher-order elements developed in the framework of Carrera Unified Formulation (CUF). Besides, a modified Mazars damage method incorporating fracture energy regularization is employed to describe the nonlinear damage behavior of the concrete. To evaluate the efficiency of the proposed crack band formulation, three experimental concrete benchmarks are selected for the numerical damage analysis. By comparing numerical and experimental results, the proposed method can guarantee mesh objectivity despite varying finite element numbers and orders, indicating perseved fracture energy consumption within proposed higher-order beam models.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"195 ","pages":"Article 103709"},"PeriodicalIF":4.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0965997824001169/pdfft?md5=018f7bfbb88f5567d5c0997b7a0ffd39&pid=1-s2.0-S0965997824001169-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of selected machine learning techniques for predicting the pull-off strength of the surface layer of eco-friendly concrete","authors":"Mateusz Moj,&nbsp;Slawomir Czarnecki","doi":"10.1016/j.advengsoft.2024.103710","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103710","url":null,"abstract":"<div><p>With recent trends reducing the carbon footprint of concrete, more novel materials are designed. It's mostly done by replacing cement with admixtures that are wastes in industrial processes. There is a need to provide reliable and accurate models to estimate the properties of the material. In this case the selected ML algorithms such as ANN, RF and DT were used for estimating the pull-off strength of the surface layer of cement mortar containing granite powder, fly ash and ground granulated blast furnace slag. The focus was on the cement-sand ratio of 1:3, replacing up to 30 % of the binder. Ultrasonic pulse velocity and pull-off strength of the surface layer. The analyses were performed in comparative manner and proved the accuracy of the designed models. The error values (MAPE, NRMSE and MAE) of the most effective model is below 3,5 %, indicating an extremely high success rate in prediction. An R² ratio of 0.9436 confirms the very good fit of the model. Parametric tests were performed and SHAP analysis gave a better understanding of the models. The main conclusion of the study is to identify the possibility of replacing destructive testing with non-destructive testing supported by machine learning and material information to determine the pull-off strength of the subsurface layer at a selected depth for cement mortars containing waste materials. A particular advantage of the presented approach is the possibility of reducing the time to determine selected desired material parameters and the amount of testing required compared to the traditional approach.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"195 ","pages":"Article 103710"},"PeriodicalIF":4.0,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438306","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":"Implementation of explanatory texts output for bridge damage in a bridge inspection web system","authors":"Pang-jo Chun ,&nbsp;Honghu Chu ,&nbsp;Kota Shitara ,&nbsp;Tatsuro Yamane ,&nbsp;Yu Maemura","doi":"10.1016/j.advengsoft.2024.103706","DOIUrl":"https://doi.org/10.1016/j.advengsoft.2024.103706","url":null,"abstract":"<div><p>Bridge photographs contain significant technical information, such as damaged structural parts and types of damage, yet interpreting these details is not always straightforward. Despite the advancements in image analysis for bridge inspection, there remains a significant gap in converting these images into comprehensible explanatory texts that can be readily used by less experienced engineers and administrative staff for effective maintenance decision-making. In this study, we developed a model that generates explanatory texts from bridge images based on a deep learning model, and we also developed a web system that can be utilized during bridge inspections. The proposed method enables the provision of user-friendly, text-based explanations of bridge damage within images, allowing relatively inexperienced engineers and administrative staff without extensive technical expertise to understand the representation of bridge damage in text form. Additionally, we have developed a system that continually trains and improves its performance by accumulating data as users interact with it. This paper describes the image captioning technique for generating explanatory texts and the structure of the web system.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"195 ","pages":"Article 103706"},"PeriodicalIF":4.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0965997824001133/pdfft?md5=939648364f624a38c53552eb9cd46e6f&pid=1-s2.0-S0965997824001133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141438307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}