Kefu Yao, Zhiping Wen, Chenfei Shao, Jiaquan Yang, Huaizhi Su
The pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.
{"title":"A multisource data‐driven monitoring model for assessing concrete dam behavior","authors":"Kefu Yao, Zhiping Wen, Chenfei Shao, Jiaquan Yang, Huaizhi Su","doi":"10.1111/mice.13232","DOIUrl":"https://doi.org/10.1111/mice.13232","url":null,"abstract":"The pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140953912","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}
This study explores artificial intelligence (AI) for shear wall layout design, aiming to overcome challenges in data feature sparsity and the complexity of drawing representations in existing AI‐based methods. We pioneer an innovative method leveraging the potential of diffusion models, establishing a suitable drawing representation, and examining the impact of various conditions. The proposed image‐prompt diffusion model incorporating a mask tensor featuring tailored training methods demonstrates superior feature extraction and design effectiveness. A comparative study reveals the advanced capabilities of the Struct‐Diffusion model in capturing engineering designs and optimizing performance metrics such as inter‐story drift ratio (in elastic analysis), offering significant improvements over previous methods and paving the way for future innovations in intelligent designs.
{"title":"Intelligent design of shear wall layout based on diffusion models","authors":"Yi Gu, Yuli Huang, Wenjie Liao, Xinzheng Lu","doi":"10.1111/mice.13236","DOIUrl":"https://doi.org/10.1111/mice.13236","url":null,"abstract":"This study explores artificial intelligence (AI) for shear wall layout design, aiming to overcome challenges in data feature sparsity and the complexity of drawing representations in existing AI‐based methods. We pioneer an innovative method leveraging the potential of diffusion models, establishing a suitable drawing representation, and examining the impact of various conditions. The proposed image‐prompt diffusion model incorporating a mask tensor featuring tailored training methods demonstrates superior feature extraction and design effectiveness. A comparative study reveals the advanced capabilities of the Struct‐Diffusion model in capturing engineering designs and optimizing performance metrics such as inter‐story drift ratio (in elastic analysis), offering significant improvements over previous methods and paving the way for future innovations in intelligent designs.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140954036","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}
D. Shang, Shuai Su, Y. K. Sun, F. Wang, Y. Cao, W. F. Yang, P. Li, J. H. Zhou
The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.
{"title":"Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method","authors":"D. Shang, Shuai Su, Y. K. Sun, F. Wang, Y. Cao, W. F. Yang, P. Li, J. H. Zhou","doi":"10.1111/mice.13235","DOIUrl":"https://doi.org/10.1111/mice.13235","url":null,"abstract":"The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140949456","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}
Fan Ding, Yongyi Zhang, Jiankun Peng, Yuming Ge, Tao Qu, Xingyuan Tao, Jun Chen
The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication purposes and are not directly suitable for transportation research due to issues such as low spatial precision, sparse sampling granularity, and lacking traffic semantic features. This article proposes a Hybrid model for identifying individual intercity transport modes based on CSD. Several multidimensional mobility features are proposed that extract interpretable motion characteristics from CSD. A preliminary transport mode probability judgment is made based on the mobility features. Then, the complete transport mode is confirmed considering the temporal continuity correlation of the entire trace. Experiments confirm the Hybrid model's superior precision in identifying transport modes over baseline models, with an average F1 score of 0.92, maintaining high accuracy across various trajectory lengths. This model would support further studying individual intercity travel behavior patterns, aiding transportation planning and operational management decisions using CSD.
{"title":"A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data","authors":"Fan Ding, Yongyi Zhang, Jiankun Peng, Yuming Ge, Tao Qu, Xingyuan Tao, Jun Chen","doi":"10.1111/mice.13229","DOIUrl":"https://doi.org/10.1111/mice.13229","url":null,"abstract":"The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication purposes and are not directly suitable for transportation research due to issues such as low spatial precision, sparse sampling granularity, and lacking traffic semantic features. This article proposes a Hybrid model for identifying individual intercity transport modes based on CSD. Several multidimensional mobility features are proposed that extract interpretable motion characteristics from CSD. A preliminary transport mode probability judgment is made based on the mobility features. Then, the complete transport mode is confirmed considering the temporal continuity correlation of the entire trace. Experiments confirm the Hybrid model's superior precision in identifying transport modes over baseline models, with an average F1 score of 0.92, maintaining high accuracy across various trajectory lengths. This model would support further studying individual intercity travel behavior patterns, aiding transportation planning and operational management decisions using CSD.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140920026","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}
The results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed method, including detection, filtering, and classification models, was trained and validated based on photographs collected from EDC surveys in Uki City, Kumamoto Prefecture. Then, a software system, which deployed these models, was developed for the onsite EDC surveyors to detect damages shown in the photographs of the surveyed house and classify damage levels. The test results based on 32 target buildings indicate that the detection model achieved high recall in detecting damage. Moreover, the redundant detected regions can be precisely filtered by the filtering model. Finally, the classification model achieved relatively high overall accuracy in classifying the damage level.
地震破坏认证（EDC）调查的结果是改善灾民生活的支持措施的基础。为了解决进行 EDC 调查的劳动力有限和损坏程度判断困难等问题，提出了一种使用多重卷积神经网络模型的木质房屋损坏检测和等级分类方法。该方法包括检测、过滤和分类模型，根据在熊本县宇喜市的 EDC 调查中收集的照片进行了训练和验证。然后，开发了一个部署了这些模型的软件系统，供现场 EDC 勘测人员检测勘测房屋照片中显示的损坏情况，并对损坏程度进行分类。基于 32 栋目标建筑物的测试结果表明，该检测模型在检测损坏方面实现了高召回率。此外，滤波模型还能精确过滤多余的检测区域。最后，分类模型在对损坏程度进行分类时取得了相对较高的整体准确率。
{"title":"Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos","authors":"Kai Wu, Masashi Matsuoka, Haruki Oshio","doi":"10.1111/mice.13224","DOIUrl":"https://doi.org/10.1111/mice.13224","url":null,"abstract":"The results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed method, including detection, filtering, and classification models, was trained and validated based on photographs collected from EDC surveys in Uki City, Kumamoto Prefecture. Then, a software system, which deployed these models, was developed for the onsite EDC surveyors to detect damages shown in the photographs of the surveyed house and classify damage levels. The test results based on 32 target buildings indicate that the detection model achieved high recall in detecting damage. Moreover, the redundant detected regions can be precisely filtered by the filtering model. Finally, the classification model achieved relatively high overall accuracy in classifying the damage level.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140915212","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}
The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high-accuracy models are still the main challenges required to be addressed. Therefore, this research presents a lightweight feature attention fusion network for pavement crack segmentation. This structure employs FasterNet as the backbone network, ensuring performance while reducing model inference time and memory overhead. Additionally, the receptive field block is incorporated to simulate human visual perception, enhancing the network's feature extraction capability. Ultimately, our approach employs the feature fusion module (FFM) to effectively combine decoder outputs with encoder's low-level features using weight vectors. Experimental results on public crack datasets, namely, CFD, CRACK500, and DeepCrack, demonstrate that compared to other semantic segmentation algorithms, the proposed method achieves both accurate and comprehensive pavement crack extraction while ensuring speed.
{"title":"A lightweight feature attention fusion network for pavement crack segmentation","authors":"Yucheng Huang, Yuchen Liu, Fang Liu, Wei Liu","doi":"10.1111/mice.13225","DOIUrl":"https://doi.org/10.1111/mice.13225","url":null,"abstract":"The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high-accuracy models are still the main challenges required to be addressed. Therefore, this research presents a lightweight feature attention fusion network for pavement crack segmentation. This structure employs FasterNet as the backbone network, ensuring performance while reducing model inference time and memory overhead. Additionally, the receptive field block is incorporated to simulate human visual perception, enhancing the network's feature extraction capability. Ultimately, our approach employs the feature fusion module (FFM) to effectively combine decoder outputs with encoder's low-level features using weight vectors. Experimental results on public crack datasets, namely, CFD, CRACK500, and DeepCrack, demonstrate that compared to other semantic segmentation algorithms, the proposed method achieves both accurate and comprehensive pavement crack extraction while ensuring speed.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881303","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}
Jun Su Park, Taehoon Hong, Dong-Eun Lee, Hyo Seon Park
This study introduces the constraint-aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness-governed structures, size optimization is crucial for strength-governed structures. Optimizing size, shape, and topology together consistently leads to the most weight-efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.
本研究介绍了约束感知优化模型（CAOM），这是一种新型优化框架，旨在同时优化平面桁架结构的尺寸、形状和拓扑结构。传统的优化模型依赖于梯度下降，由于依赖于单一的优化代理，在管理各种约束条件时经常会遇到困难，与此不同，CAOM 有效地解决了这一难题。CAOM 将各种辅助模块与 Adam 优化器（梯度下降法的一种变体）结合在一起，从而有效地解决了这一难题。与众不同的是，CAOM 采用了超出神经网络传统用途的泄漏整流线性单元（ReLU）激活函数，将其作为一种机制来无缝整合约束和损失。该模型的有效性通过两个数值示例和一个实际应用得到了验证，表明与未优化的设计相比，CAOM 可在完全遵守结构、尺寸和可移动约束的前提下将结构重量减轻 84%。此外，研究还发现，形状优化对刚度控制结构起着关键作用，而尺寸优化则对强度控制结构至关重要。同时对尺寸、形状和拓扑结构进行优化，始终能获得最省力的设计。这强调了在优化过程中采用整体方法的重要性。
{"title":"Constraint-aware optimization model for plane truss structures via single-agent gradient descent","authors":"Jun Su Park, Taehoon Hong, Dong-Eun Lee, Hyo Seon Park","doi":"10.1111/mice.13226","DOIUrl":"https://doi.org/10.1111/mice.13226","url":null,"abstract":"This study introduces the constraint-aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness-governed structures, size optimization is crucial for strength-governed structures. Optimizing size, shape, and topology together consistently leads to the most weight-efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881304","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}
Microimage strain sensing (MISS) is a novel piston-type sensor based on microscopic vision. In this study, optical disc slice is used as information carriers to improve MISS. There are multiple pits on the surface of an optical disc. By using machine vision algorithms, the pits can be converted into digital information, making them scales for recording displacements. By this means, we proposed a sensing method that combines high resolution, wide range, and strong robustness. The study measured displacement under different conditions. To address inevitable factors such as pixel drift, and manufacturing errors, corresponding compensation methods were provided. The results show that the measurements closely match those of the linear variable differential transformer, with a resolution of up to 20 nm and a range approaching the sensor size. Despite the sensor's dependence on machine vision, it demonstrates strong resistance to environmental factors such as brightness and angle. Combining compensation methods for pixel drift, and manufacturing errors, this sensor can be well-applied in various working conditions.
{"title":"Displacement sensing based on microscopic vision with high resolution and large measuring range","authors":"Pengfei Wu, Weijie Li, Xuefeng Zhao","doi":"10.1111/mice.13227","DOIUrl":"https://doi.org/10.1111/mice.13227","url":null,"abstract":"Microimage strain sensing (MISS) is a novel piston-type sensor based on microscopic vision. In this study, optical disc slice is used as information carriers to improve MISS. There are multiple pits on the surface of an optical disc. By using machine vision algorithms, the pits can be converted into digital information, making them scales for recording displacements. By this means, we proposed a sensing method that combines high resolution, wide range, and strong robustness. The study measured displacement under different conditions. To address inevitable factors such as pixel drift, and manufacturing errors, corresponding compensation methods were provided. The results show that the measurements closely match those of the linear variable differential transformer, with a resolution of up to 20 nm and a range approaching the sensor size. Despite the sensor's dependence on machine vision, it demonstrates strong resistance to environmental factors such as brightness and angle. Combining compensation methods for pixel drift, and manufacturing errors, this sensor can be well-applied in various working conditions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140846260","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}
Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic
In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block-based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated -folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real-time support for decision making in damaged masonry structures.
{"title":"Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls","authors":"Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic","doi":"10.1111/mice.13212","DOIUrl":"https://doi.org/10.1111/mice.13212","url":null,"abstract":"In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block-based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated <span data-altimg=\"/cms/asset/73c1e1c4-9d95-4765-b87a-eb7141100bb6/mice13212-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"159\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13212-math-0001.png\"><mjx-semantics><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"k\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13212:mice13212-math-0001\" display=\"inline\" location=\"graphic/mice13212-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"latinletter\" data-semantic-speech=\"k\" data-semantic-type=\"identifier\">k</mi>$k$</annotation></semantics></math></mjx-assistive-mml></mjx-container>-folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real-time support for decision making in damaged masonry structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819777","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}
Zili Wang, Panchamy Krishnakumari, Kumar Anupam, Hans van Lint, Sandra Erkens
The relationship between real-world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.
{"title":"A causal discovery approach to study key mixed traffic-related factors and age of highway affecting raveling","authors":"Zili Wang, Panchamy Krishnakumari, Kumar Anupam, Hans van Lint, Sandra Erkens","doi":"10.1111/mice.13222","DOIUrl":"https://doi.org/10.1111/mice.13222","url":null,"abstract":"The relationship between real-world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial data sets. Findings indicate a nonlinear relationship between traffic volume and raveling, with road age emerging as a shared contributor. The results also suggest that the degree to which different vehicle types contribute as a causal factor for raveling varies with carriageway configurations and lane characteristics. This underlines the need for targeted maintenance strategies. Challenges remain due to confounding correlations among traffic variables, necessitating further development of causal discovery models. This study may not conclusively resolve the debate on to what extent traffic contributes to raveling, but we argue we provide sufficient evidence against rejecting this hypothesis.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819874","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}
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