The longevity of porous asphalt pavement is inevitably compromised by the clogging of voids by various particles, leading to a degradation in its drainage function. Numerical simulations with real pore structures were used to investigate the clogging behavior of porous asphalt concrete (PAC) to clearly and intuitively understand its void clogging process. In this study, a three-dimensional model of the real void was created by computed tomography scanning. The change before and after void clogging of PAC was characterized by seepage pressure and seepage velocity in the seepage field. The computational fluid dynamics-discrete element method coupling method was used to visually describe the dynamic evolution of clogging particles in porous asphalt voids. Findings reveal that the most influential particle size for clogging in PAC-13 with 18% and 20% porosity ranged between 0.15 and 0.6 mm. In contrast, for PAC-13 with 25% porosity, the sensitive size was 0.3–1.18 mm. When clogging occurred, large particles predominantly obstructed the void inlets, prompting a refinement in the void structure. Subsequent particles either traversed the void, accumulating at the entrances of finer voids, or filled up progressively, leading to eventual clogging. Small particles either exited directly through the voids or accumulated in the bends of the voids, making the voids clogged directly. Consequently, the clogging behavior of porous asphalt was classified into three types: surface-filling clogging, void refining filter clogging, and void bending or semi-connecting clogging. These findings provide a scientific basis for optimizing PAC design and developing conservation strategies.
{"title":"Evolution of clogging of porous asphalt concrete in the seepage process through integration of computer tomography, computational fluid dynamics, and discrete element method","authors":"Bo Li, Yunpeng Zhang, Dingbang Wei, Tengfei Yao, Yongping Hu, Hui Dou","doi":"10.1111/mice.13419","DOIUrl":"https://doi.org/10.1111/mice.13419","url":null,"abstract":"The longevity of porous asphalt pavement is inevitably compromised by the clogging of voids by various particles, leading to a degradation in its drainage function. Numerical simulations with real pore structures were used to investigate the clogging behavior of porous asphalt concrete (PAC) to clearly and intuitively understand its void clogging process. In this study, a three-dimensional model of the real void was created by computed tomography scanning. The change before and after void clogging of PAC was characterized by seepage pressure and seepage velocity in the seepage field. The computational fluid dynamics-discrete element method coupling method was used to visually describe the dynamic evolution of clogging particles in porous asphalt voids. Findings reveal that the most influential particle size for clogging in PAC-13 with 18% and 20% porosity ranged between 0.15 and 0.6 mm. In contrast, for PAC-13 with 25% porosity, the sensitive size was 0.3–1.18 mm. When clogging occurred, large particles predominantly obstructed the void inlets, prompting a refinement in the void structure. Subsequent particles either traversed the void, accumulating at the entrances of finer voids, or filled up progressively, leading to eventual clogging. Small particles either exited directly through the voids or accumulated in the bends of the voids, making the voids clogged directly. Consequently, the clogging behavior of porous asphalt was classified into three types: surface-filling clogging, void refining filter clogging, and void bending or semi-connecting clogging. These findings provide a scientific basis for optimizing PAC design and developing conservation strategies.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936549","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 cover image is based on the article A rendering-based lightweight network for segmentation of high-resolution crack images by Weiwei Chen et al., https://doi.org/10.1111/mice.13290.
{"title":"Cover Image, Volume 40, Issue 3","authors":"","doi":"10.1111/mice.13416","DOIUrl":"10.1111/mice.13416","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>A rendering-based lightweight network for segmentation of high-resolution crack images</i> by Weiwei Chen et al., https://doi.org/10.1111/mice.13290.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 3","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935159","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}
Jifu Guo, Shengguang Bai, Xun Li, Kai Xian, Erjian Liu, Wenting Ding, Xizhi Ma
This study primarily focuses on generating mobility flow in regions and cities, which plays an important role in urban planning and management. The majority of existing mobility flow models, including conventional statistical models and deep learning-based models, are heavily dependent on historical data to predict future mobility flows. The application of these models poses significant challenges in the planning and construction of emerging cities and regions, particularly in developing countries experiencing swift urbanization. These challenges are exacerbated by a dearth of historical data and rapid shifts in mobility patterns. Consequently, the scenario necessitates a mobility flow generation model capable of generating flows without historical data. This study introduces the universal geography neural network, an algorithm designed to glean potential patterns in human mobility across diverse cities and temporal spans. This is achieved through the analysis of substantial quantities of location-based data, resulting in the generation of mobility flows within a city. Our experiment, designed to extract various features and generate fine-grained mobility flows in the testing set, outperforms both traditional models and state-of-the-art deep learning models. Moreover, our model has proven capable of generating reliable results across various time periods and grid areas.
{"title":"A universal geography neural network for mobility flow prediction in planning scenarios","authors":"Jifu Guo, Shengguang Bai, Xun Li, Kai Xian, Erjian Liu, Wenting Ding, Xizhi Ma","doi":"10.1111/mice.13398","DOIUrl":"https://doi.org/10.1111/mice.13398","url":null,"abstract":"This study primarily focuses on generating mobility flow in regions and cities, which plays an important role in urban planning and management. The majority of existing mobility flow models, including conventional statistical models and deep learning-based models, are heavily dependent on historical data to predict future mobility flows. The application of these models poses significant challenges in the planning and construction of emerging cities and regions, particularly in developing countries experiencing swift urbanization. These challenges are exacerbated by a dearth of historical data and rapid shifts in mobility patterns. Consequently, the scenario necessitates a mobility flow generation model capable of generating flows without historical data. This study introduces the universal geography neural network, an algorithm designed to glean potential patterns in human mobility across diverse cities and temporal spans. This is achieved through the analysis of substantial quantities of location-based data, resulting in the generation of mobility flows within a city. Our experiment, designed to extract various features and generate fine-grained mobility flows in the testing set, outperforms both traditional models and state-of-the-art deep learning models. Moreover, our model has proven capable of generating reliable results across various time periods and grid areas.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"7 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935158","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}
For existing bridge weigh-in-motion technologies, the main challenge in accurate weight estimation is to overcome the difficulty of identifying the closely spaced axles. To do so, many field test data are generally required for each bridge in application. To address such a challenge, a novel two-level sequential artificial neural network (ANN) model trained by the hybrid simulated-experimental data was proposed in this study to identify the gross weight and axle weight. For this, simulations and scaled experiments were conducted for the vehicle–bridge interaction system to develop the sequential ANN model. The sequential ANN model was realized by a special data looping strategy, in which the outputs of the global-level ANN served as partial inputs to the following local-level ANN to predict the axle weight. The optimized size of the training data and the appropriate hybrid ratio of the sequential ANN model were also explored. Finally, the proposed algorithm was applied to a real bridge application via transfer learning, as the optimized hybrid sequential ANN model serves as the pre-trained model. The results showed that for the small training datasets with only 5% experimental data, the proposed algorithm significantly improved the accuracy in weight estimation of moving vehicles with closely spaced axles. The field test demonstrated that the proposed algorithm also applies to different bridges within a gross weight identification error of 5%, showing the promise of the proposed algorithm in practical applications.
{"title":"Hybrid-data-driven bridge weigh-in-motion technology using a two-level sequential artificial neural network","authors":"Wangchen Yan, Hao Ren, Xin Luo, Shaofan Li","doi":"10.1111/mice.13415","DOIUrl":"https://doi.org/10.1111/mice.13415","url":null,"abstract":"For existing bridge weigh-in-motion technologies, the main challenge in accurate weight estimation is to overcome the difficulty of identifying the closely spaced axles. To do so, many field test data are generally required for each bridge in application. To address such a challenge, a novel two-level sequential artificial neural network (ANN) model trained by the hybrid simulated-experimental data was proposed in this study to identify the gross weight and axle weight. For this, simulations and scaled experiments were conducted for the vehicle–bridge interaction system to develop the sequential ANN model. The sequential ANN model was realized by a special data looping strategy, in which the outputs of the global-level ANN served as partial inputs to the following local-level ANN to predict the axle weight. The optimized size of the training data and the appropriate hybrid ratio of the sequential ANN model were also explored. Finally, the proposed algorithm was applied to a real bridge application via transfer learning, as the optimized hybrid sequential ANN model serves as the pre-trained model. The results showed that for the small training datasets with only 5% experimental data, the proposed algorithm significantly improved the accuracy in weight estimation of moving vehicles with closely spaced axles. The field test demonstrated that the proposed algorithm also applies to different bridges within a gross weight identification error of 5%, showing the promise of the proposed algorithm in practical applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929496","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}
Ali Hassandokht Mashhadi, Abbas Rashidi, Masoud Hamedi, Nikola Marković
Accurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in traffic data, making accurate volume estimation difficult. Addressing this, our study utilized a custom regularized model and variational autoencoders (VAE) to generate synthetic data that improves traffic volume estimations in these challenging areas. The proposed method not only bridges the data gaps between sparse CCS sensors but also outperforms several benchmark models, as measured by mean absolute percentage error, root mean square error, and mean absolute error. Moreover, the effectiveness of VAE-augmented models in enhancing the precision and accuracy of traffic volume estimations further underscores the benefits of integrating synthetic data into traffic-modeling approaches. These findings highlight the potential of the proposed approach to enhance traffic volume estimation in construction work zones and assist transportation agencies in making informed decisions for traffic management.
{"title":"Traffic estimation in work zones using a custom regression model and data augmentation","authors":"Ali Hassandokht Mashhadi, Abbas Rashidi, Masoud Hamedi, Nikola Marković","doi":"10.1111/mice.13413","DOIUrl":"https://doi.org/10.1111/mice.13413","url":null,"abstract":"Accurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in traffic data, making accurate volume estimation difficult. Addressing this, our study utilized a custom regularized model and variational autoencoders (VAE) to generate synthetic data that improves traffic volume estimations in these challenging areas. The proposed method not only bridges the data gaps between sparse CCS sensors but also outperforms several benchmark models, as measured by mean absolute percentage error, root mean square error, and mean absolute error. Moreover, the effectiveness of VAE-augmented models in enhancing the precision and accuracy of traffic volume estimations further underscores the benefits of integrating synthetic data into traffic-modeling approaches. These findings highlight the potential of the proposed approach to enhance traffic volume estimation in construction work zones and assist transportation agencies in making informed decisions for traffic management.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"5 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929495","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}
Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim, Xiaowei Deng
This study developed Geometry Physics neural Operator (GPO), a novel solver framework to approximate the partial differential equation (PDE) solutions for solid mechanics problems with irregular geometry and achieved a significant speedup in simulation time compared to numerical solvers. GPO leverages a weak form of PDEs based on the principle of least work, incorporates geometry information, and imposes exact Dirichlet boundary conditions within the network architecture to attain accurate and efficient modeling. This study focuses on applying GPO to model the behaviors of complicated bodies without any guided solutions or labeled training data. GPO adopts a modified Fourier neural operator as the backbone to achieve significantly improved convergence speed and to learn the complicated solution field of solid mechanics problems. Numerical experiments involved a two-dimensional plane with a hole and a three-dimensional building structure with Dirichlet boundary constraints. The results indicate that the geometry layer and exact boundary constraints in GPO significantly contribute to the convergence accuracy and speed, outperforming the previous benchmark in simulations of irregular geometry. The comparison results also showed that GPO can converge to solution fields faster than a commercial numerical solver in the structural examples. Furthermore, GPO demonstrates stronger performance than the solvers when the mesh size is smaller, and it achieves over 3<span data-altimg="/cms/asset/df3c163a-e7c6-4e48-9813-d4de41ec9066/mice13405-math-0001.png"></span><mjx-container ctxtmenu_counter="93" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/mice13405-math-0001.png"><mjx-semantics><mjx-mo data-semantic- data-semantic-role="unknown" data-semantic-speech="times" data-semantic-type="operator"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:10939687:media:mice13405:mice13405-math-0001" display="inline" location="graphic/mice13405-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mo data-semantic-="" data-semantic-role="unknown" data-semantic-speech="times" data-semantic-type="operator">×</mo>$times$</annotation></semantics></math></mjx-assistive-mml></mjx-container> and 2<span data-altimg="/cms/asset/67f630b9-5c45-4ee8-a436-0fef5e94744b/mice13405-math-0002.png"></span><mjx-container ctxtmenu_counter="94" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/mice13405-math-0002.png"><mjx-semantics><mjx-mo data-semantic- data-semantic-role="unknown" data-semantic-speech="times" data-semantic-type="operator"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math
{"title":"Geometry physics neural operator solver for solid mechanics","authors":"Chawit Kaewnuratchadasorn, Jiaji Wang, Chul-Woo Kim, Xiaowei Deng","doi":"10.1111/mice.13405","DOIUrl":"https://doi.org/10.1111/mice.13405","url":null,"abstract":"This study developed Geometry Physics neural Operator (GPO), a novel solver framework to approximate the partial differential equation (PDE) solutions for solid mechanics problems with irregular geometry and achieved a significant speedup in simulation time compared to numerical solvers. GPO leverages a weak form of PDEs based on the principle of least work, incorporates geometry information, and imposes exact Dirichlet boundary conditions within the network architecture to attain accurate and efficient modeling. This study focuses on applying GPO to model the behaviors of complicated bodies without any guided solutions or labeled training data. GPO adopts a modified Fourier neural operator as the backbone to achieve significantly improved convergence speed and to learn the complicated solution field of solid mechanics problems. Numerical experiments involved a two-dimensional plane with a hole and a three-dimensional building structure with Dirichlet boundary constraints. The results indicate that the geometry layer and exact boundary constraints in GPO significantly contribute to the convergence accuracy and speed, outperforming the previous benchmark in simulations of irregular geometry. The comparison results also showed that GPO can converge to solution fields faster than a commercial numerical solver in the structural examples. Furthermore, GPO demonstrates stronger performance than the solvers when the mesh size is smaller, and it achieves over 3<span data-altimg=\"/cms/asset/df3c163a-e7c6-4e48-9813-d4de41ec9066/mice13405-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"93\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13405-math-0001.png\"><mjx-semantics><mjx-mo data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13405:mice13405-math-0001\" display=\"inline\" location=\"graphic/mice13405-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mo data-semantic-=\"\" data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\">×</mo>$times$</annotation></semantics></math></mjx-assistive-mml></mjx-container> and 2<span data-altimg=\"/cms/asset/67f630b9-5c45-4ee8-a436-0fef5e94744b/mice13405-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"94\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13405-math-0002.png\"><mjx-semantics><mjx-mo data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"times\" data-semantic-type=\"operator\"><mjx-c></mjx-c></mjx-mo></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math ","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"73 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924496","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}
Suspended ceiling systems constitute a pivotal non-structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi-stage warped panel detection (MWPD) method to automatically locate warped panels from two-dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel-level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on-site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%.
{"title":"Multi-stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification","authors":"Qinghua Guo, Weihang Gao, Qingzhao Kong, Xilin Lu","doi":"10.1111/mice.13414","DOIUrl":"https://doi.org/10.1111/mice.13414","url":null,"abstract":"Suspended ceiling systems constitute a pivotal non-structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi-stage warped panel detection (MWPD) method to automatically locate warped panels from two-dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel-level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on-site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"28 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924497","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}
Manxu Zhou, Guanting Ye, Ka-Veng Yuen, Wenhao Yu, Qiang Jin
Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large-scale reinforcing rib—require high-precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self-built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP50) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels.
{"title":"A graph attention reasoning model for prefabricated component detection","authors":"Manxu Zhou, Guanting Ye, Ka-Veng Yuen, Wenhao Yu, Qiang Jin","doi":"10.1111/mice.13373","DOIUrl":"https://doi.org/10.1111/mice.13373","url":null,"abstract":"Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the internal structural complexities of these components. Prefabricated composite panels, due to their complex structure—including small embedded parts and large-scale reinforcing rib—require high-precision, multiscale feature recognition. This study developed an instance segmentation model: a graph attention reasoning model (GARM) for prefabricated component detection, for the quality inspection of prefabricated concrete composite panels. First, a dataset of prefabricated concrete composite components was constructed to address the shortage of existing data and provide sufficient samples for training the segmentation network. Subsequently, after training on a self-built dataset of prefabricated concrete composite panels, ablation experiments and comparative tests were conducted. The GARM segmentation model demonstrated superior performance in terms of detection speed and model lightweighting. Its accuracy surpassed other models, with a mean average precision (mAP<sub>50</sub>) of 88.7%. This study confirms the efficacy and reliability of the GARM instance segmentation model in detecting prefabricated concrete composite panels.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911799","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 cover image is based on the article Hybrid structural analysis integrating physical model and continuous-time state-space neural network model by Yi-QIng Ni et al., https://doi.org/10.1111/mice.13282.
{"title":"Cover Image, Volume 40, Issue 2","authors":"","doi":"10.1111/mice.13410","DOIUrl":"10.1111/mice.13410","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Hybrid structural analysis integrating physical model and continuous-time state-space neural network model</i> by Yi-QIng Ni et al., https://doi.org/10.1111/mice.13282.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 2","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908265","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}
Over the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network-level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi-year pavement images have not been fully utilized for analyzing detailed pavement deterioration. One challenge is the accurate and efficient registration of multi-temporal pavement images. This study pioneers the use of feature-based methods to address this challenge. It evaluates various feature-based image registration methods, including both state-of-the-art and novel combinations of feature detectors and descriptors. These methods are rigorously assessed using hybrid “step-by-step” and “end-to-end” performance evaluation metrics, with a ground reference dataset containing 100 pavement image pairs featuring diverse crack types and varying year gaps. The results confirm the feasibility of using feature-based techniques to register multi-temporal pavement images. A novel combination of the AKAZE detector and the Binary Robust Independent Elementary Features (BRIEF) descriptor was identified as the best-performing method, successfully registering 96 out of 100 image pairs. This advancement enables pavement engineers to accurately monitor pavement deterioration using multi-temporal images.
{"title":"A feature-based pavement image registration method for precise pavement deterioration monitoring","authors":"Zhongyu Yang, Mohsen Mohammadi, Haolin Wang, Yi-Chang (James) Tsai","doi":"10.1111/mice.13407","DOIUrl":"https://doi.org/10.1111/mice.13407","url":null,"abstract":"Over the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network-level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi-year pavement images have not been fully utilized for analyzing detailed pavement deterioration. One challenge is the accurate and efficient registration of multi-temporal pavement images. This study pioneers the use of feature-based methods to address this challenge. It evaluates various feature-based image registration methods, including both state-of-the-art and novel combinations of feature detectors and descriptors. These methods are rigorously assessed using hybrid “step-by-step” and “end-to-end” performance evaluation metrics, with a ground reference dataset containing 100 pavement image pairs featuring diverse crack types and varying year gaps. The results confirm the feasibility of using feature-based techniques to register multi-temporal pavement images. A novel combination of the AKAZE detector and the Binary Robust Independent Elementary Features (BRIEF) descriptor was identified as the best-performing method, successfully registering 96 out of 100 image pairs. This advancement enables pavement engineers to accurately monitor pavement deterioration using multi-temporal images.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"134 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905283","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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