Computer-Aided Civil and Infrastructure Engineering最新文献_第8页

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction 基于车桥相互作用同步统计力矩理论的桥梁损伤识别

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-24 DOI: 10.1111/mice.13298

Yang Yang, Wenming Xu, Anguo Gao, Qingshan Yang, Yuqing Gao

{"title":"Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction","authors":"Yang Yang, Wenming Xu, Anguo Gao, Qingshan Yang, Yuqing Gao","doi":"10.1111/mice.13298","DOIUrl":"https://doi.org/10.1111/mice.13298","url":null,"abstract":"Considering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data-driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two-axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second-order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first-order modal shape curve is constructed using the second-order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on-site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multicategory fire damage detection of post‐fire reinforced concrete structural components 火灾后钢筋混凝土结构部件的多类别火损检测

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-24 DOI: 10.1111/mice.13314

Pengfei Wang, Caiwei Liu, Xinyu Wang, Libin Tian, Jijun Miao, Yanchun Liu

{"title":"Multicategory fire damage detection of post‐fire reinforced concrete structural components","authors":"Pengfei Wang, Caiwei Liu, Xinyu Wang, Libin Tian, Jijun Miao, Yanchun Liu","doi":"10.1111/mice.13314","DOIUrl":"https://doi.org/10.1111/mice.13314","url":null,"abstract":"This paper introduces an enhanced you only look once (YOLO) v5s‐D network customized for detecting various categories of damage to post‐fire reinforced concrete (RC) components. These damage types encompass surface soot, cracks, concrete spalling, and rebar exposure. A dataset containing 1536 images depicting damaged RC components was compiled. By integrating ShuffleNet, adaptive attention mechanisms, and a feature enhancement module, the capability of the network for multi‐scale feature extraction in complex backgrounds was improved, alongside a reduction in model parameters. Consequently, YOLOv5s‐D achieved a detection accuracy of 93%, marking an 11% enhancement over the baseline YOLOv5s network. Comparison and ablation tests conducted on different modules, varying dataset sizes, against other state‐of‐the‐art networks, and on public datasets validate the resilience, superiority, and generalization capability of YOLOv5s‐D. Finally, an application leveraging YOLOv5s‐D was developed and integrated into a mobile device to facilitate real‐time detection of post‐fire damaged RC components. This application can integrate diverse fire scenarios and data types, expanding its scope in future. The proposed detection method compensates for the subjective limitations of manual inspections, providing a reference for damage assessment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"10 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A neural network-based automated methodology to identify the crack causes in masonry structures 基于神经网络的砌体结构裂缝成因自动识别方法

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-22 DOI: 10.1111/mice.13311

A. Iannuzzo, V. Musone, E. Ruocco

引用次数: 0

Cover Image, Volume 39, Issue 15 封面图片，第 39 卷第 15 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-22 DOI: 10.1111/mice.13309

引用次数: 0

Cover Image, Volume 39, Issue 15 封面图片，第 39 卷第 15 期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-22 DOI: 10.1111/mice.13308

引用次数: 0

An efficient static solver for the lattice discrete particle model 晶格离散粒子模型的高效静态求解器

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-15 DOI: 10.1111/mice.13306

Dongge Jia, John C. Brigham, Alessandro Fascetti

{"title":"An efficient static solver for the lattice discrete particle model","authors":"Dongge Jia, John C. Brigham, Alessandro Fascetti","doi":"10.1111/mice.13306","DOIUrl":"10.1111/mice.13306","url":null,"abstract":"The lattice discrete particle model (LDPM) has been proven to be one of the most appealing computational tools to simulate fracture in quasi-brittle materials. Despite tremendous advancements in the definition and implementation of the method, solution strategies are still limited to dynamic algorithms, resulting in prohibitive computational costs and challenges related to solution accuracy for quasi-static conditions. This study presents a novel static solver for LDPM, introducing fundamental innovation: (1) LDPM constitutive laws are modified to provide continuous response through all possible strain/stress states; (2) an adaptive arc-length method is proposed in combination with a criterion to select the sign of the iterative load factor; (3) an adaptive limit-unloading–reloading path switch algorithm is proposed to restrict oscillations in the global stiffness matrix. Extensive validation of the proposed approach is presented. Numerical results demonstrate that the static solver exhibits satisfactory convergence rates, significantly outperforming available dynamic solutions in computational efficiency.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 23","pages":"3531-3551"},"PeriodicalIF":8.5,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141625195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A non-contact identification method of overweight vehicles based on computer vision and deep learning 基于计算机视觉和深度学习的超重车辆非接触式识别方法

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-12 DOI: 10.1111/mice.13299

Daoheng Li, Meiyu Liu, Lu Yang, Han Wei, Jie Guo

{"title":"A non-contact identification method of overweight vehicles based on computer vision and deep learning","authors":"Daoheng Li, Meiyu Liu, Lu Yang, Han Wei, Jie Guo","doi":"10.1111/mice.13299","DOIUrl":"10.1111/mice.13299","url":null,"abstract":"The phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle weighing systems can only meet some of the requirements of different scenarios. The development of artificial intelligence technologies, especially deep learning, has greatly enhanced the accuracy and efficiency of computer vision. To this end, the paper proposes a method using computer vision and deep learning for the non-contact identification of overweight vehicles. By constructing two deep learning models and combining them with the vehicle vibration model and relevant specifications, the weight and maximum allowable weight of the vehicle are obtained to make a comparison for determining overweight. Experimental verification was performed using a two-axle vehicle as an illustrative example, and the results demonstrate that the proposed method exhibits excellent feasibility and effectiveness. It shows significant potential in real-world scenarios, laying a research foundation for practical engineering applications. Additionally, it provides a reference for the governance and decision-making of overweight issues for relevant authorities.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3452-3476"},"PeriodicalIF":8.5,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed neural operator solver and super-resolution for solid mechanics 物理信息神经算子求解器和固体力学超分辨率

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-11 DOI: 10.1111/mice.13292

Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim

{"title":"Physics-informed neural operator solver and super-resolution for solid mechanics","authors":"Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim","doi":"10.1111/mice.13292","DOIUrl":"10.1111/mice.13292","url":null,"abstract":"Physics-Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics-Informed Neural Operator Solver (PINOS) to achieve accurate and fast simulations without any required data set. The training of PINOS adopts a weak form based on the principle of least work for static simulations and a strong form for dynamic systems in solid mechanics. Results from numerical examples indicated that PINOS is capable of approximating solutions notably faster than the benchmarks of PINNs in both static an dynamic systems. The comparisons also showed that PINOS reached a convergence speed of over 20 times faster than finite element software in two-dimensional and three-dimensional static problems. Furthermore, this study examined the zero-shot super-resolution capability by developing Super-Resolution PINOS (SR-PINOS) that was trained on a coarse mesh and validated on fine mesh. The numerical results demonstrate the great performance of the model to obtain accurate solutions with a speed up, suggesting effectiveness in increasing sampling points and scaling a simulation. This study also discusses the differentiation methods of PINOS and SR-PINOS and suggests potential implementations related to forward applications for promising machine learning methods for structural designs and optimization.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3435-3451"},"PeriodicalIF":8.5,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings 用于建筑物周围风压分析和重建的多分辨率动态模式分解方法

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-10 DOI: 10.1111/mice.13304

Reda Snaiki, Seyedeh Fatemeh Mirfakhar

{"title":"Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings","authors":"Reda Snaiki, Seyedeh Fatemeh Mirfakhar","doi":"10.1111/mice.13304","DOIUrl":"10.1111/mice.13304","url":null,"abstract":"Accurate wind pressure analysis on high-rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high-rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real-world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real-world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high-rise buildings, paving the way for advancements in digital twins, real-time monitoring, and risk assessment of wind loads.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3375-3391"},"PeriodicalIF":8.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based segmentation model for permeable concrete meso-structures 基于深度学习的透水混凝土中层结构分割模型

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-07-09 DOI: 10.1111/mice.13300

De Chen, Yukun Li, Jiaxing Tao, Yuchen Li, Shilong Zhang, Xuehui Shan, Tingting Wang, Zhi Qiao, Rui Zhao, Xiaoqiang Fan, Zhongrong Zhou

{"title":"Deep learning-based segmentation model for permeable concrete meso-structures","authors":"De Chen, Yukun Li, Jiaxing Tao, Yuchen Li, Shilong Zhang, Xuehui Shan, Tingting Wang, Zhi Qiao, Rui Zhao, Xiaoqiang Fan, Zhongrong Zhou","doi":"10.1111/mice.13300","DOIUrl":"10.1111/mice.13300","url":null,"abstract":"The meso-structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso-structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso-structures of pervious concrete, a method utilizing deep learning image semantic segmentation techniques is proposed in this study. First, based on the classical deep learning model, three models, namely, Res-UNet, ED-SegNet, and G-ENet, are proposed for recognizing pervious concrete meso-structure using deep learning image semantic segmentation techniques. These models introduce a residual module, a hybrid loss function, and a differential recognition branching structure to enhance the ability to recognize detailed information within pervious concrete meso-structure and small targets. Second, the respective recognition performances of these methods on the meso-structure of pervious concrete were thoroughly analyzed by experiment. The results indicate that the proposed three recognition methods for recognizing the meso-structure of permeable concrete outperform conventional techniques not only in terms of efficiency but also in recognition accuracy and the ability to distinguish and identify aggregates, pores, and cement binders. In terms of comprehensive recognition effectiveness, the Res-UNet model outperforms, followed by ED-SegNet and G-ENet. Furthermore, the computational efficiency of these three recognition methods meets the requirements of engineering applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 23","pages":"3626-3645"},"PeriodicalIF":8.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0