Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This paper presents and validates a deep learning model that predicts ductility and tensile strength from posttest images of fractured binder surfaces, with potential extensions to simplified instrumentation. The hybrid model, named PCNet, integrates a custom lightweight convolutional neural network (CNN) developed to capture local features (e.g., edges, boundaries, contours) within fracture cavities with a Swin Transformer that models global contextual dependencies. A bidirectional cross-attention fusion module is designed to facilitate mutual information exchange between CNN and transformer branches. The fused features are then processed by a fully connected network (FCN) to predict indices derived from the test. The proposed model demonstrates high predictive accuracy across a range of binders and test configurations, achieving an <span data-altimg="/cms/asset/e9fbf493-59db-40fa-93fd-5f973187445b/mice13447-math-0001.png"></span><mjx-container ctxtmenu_counter="150" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/mice13447-math-0001.png"><mjx-semantics><mjx-msup data-semantic-children="0,1" data-semantic- data-semantic-role="latinletter" data-semantic-speech="upper R squared" data-semantic-type="superscript"><mjx-mi data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic- data-semantic-parent="2" data-semantic-role="latinletter" data-semantic-type="identifier"><mjx-c></mjx-c></mjx-mi><mjx-script style="vertical-align: 0.363em;"><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number" size="s"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:10939687:media:mice13447:mice13447-math-0001" display="inline" location="graphic/mice13447-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup data-semantic-="" data-semantic-children="0,1" data-semantic-role="latinletter" data-semantic-speech="upper R squared" data-semantic-type="superscript"><mi data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="italic" data-semantic-parent="2" data-semantic-role="latinletter" data-semantic-type="identifier">R</mi><mn data-semantic-="" data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic-parent="2" data-semantic-rol
{"title":"Hybrid deep learning model for predicting failure properties of asphalt binder from fracture surface images","authors":"Babak Asadi, Viraj Shah, Abhilash Vyas, Mani Golparvar-Fard, Ramez Hajj","doi":"10.1111/mice.13447","DOIUrl":"https://doi.org/10.1111/mice.13447","url":null,"abstract":"Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This paper presents and validates a deep learning model that predicts ductility and tensile strength from posttest images of fractured binder surfaces, with potential extensions to simplified instrumentation. The hybrid model, named PCNet, integrates a custom lightweight convolutional neural network (CNN) developed to capture local features (e.g., edges, boundaries, contours) within fracture cavities with a Swin Transformer that models global contextual dependencies. A bidirectional cross-attention fusion module is designed to facilitate mutual information exchange between CNN and transformer branches. The fused features are then processed by a fully connected network (FCN) to predict indices derived from the test. The proposed model demonstrates high predictive accuracy across a range of binders and test configurations, achieving an <span data-altimg=\"/cms/asset/e9fbf493-59db-40fa-93fd-5f973187445b/mice13447-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"150\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/mice13447-math-0001.png\"><mjx-semantics><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:10939687:media:mice13447:mice13447-math-0001\" display=\"inline\" location=\"graphic/mice13447-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\">R</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-rol","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463167","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}
Yanjia Wang, Dong Yang, Ye Yuan, Jing Zhang, Francis T. K. Au
This paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications in heat transfer that focus on simple geometries, this framework uniquely addresses multi‐material domains and realistic boundary conditions through a dual‐network architecture designed for composite structures. The framework further incorporates the environmental boundary conditions of natural convection and solar radiation into the loss function and employs transfer learning for efficient adaptation to varying conditions. Moreover, a transfer learning mechanism enables rapid adaptation to new environmental states, thus markedly reducing the computations as compared to the conventional finite element method (FEM). Through noise‐augmented training and parameter identification, the TT‐PINN effectively handles the real‐world monitoring data uncertainties and allows material property calibration with limited sensor data. The framework's ability to capture complex thermal behavior is validated by studying a cable‐stayed bridge. It significantly reduces the computational costs as compared to the traditional FEM approaches.
{"title":"Hybrid physics‐informed neural network with parametric identification for modeling bridge temperature distribution","authors":"Yanjia Wang, Dong Yang, Ye Yuan, Jing Zhang, Francis T. K. Au","doi":"10.1111/mice.13436","DOIUrl":"https://doi.org/10.1111/mice.13436","url":null,"abstract":"This paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications in heat transfer that focus on simple geometries, this framework uniquely addresses multi‐material domains and realistic boundary conditions through a dual‐network architecture designed for composite structures. The framework further incorporates the environmental boundary conditions of natural convection and solar radiation into the loss function and employs transfer learning for efficient adaptation to varying conditions. Moreover, a transfer learning mechanism enables rapid adaptation to new environmental states, thus markedly reducing the computations as compared to the conventional finite element method (FEM). Through noise‐augmented training and parameter identification, the TT‐PINN effectively handles the real‐world monitoring data uncertainties and allows material property calibration with limited sensor data. The framework's ability to capture complex thermal behavior is validated by studying a cable‐stayed bridge. It significantly reduces the computational costs as compared to the traditional FEM approaches.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143435073","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}
Road cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi-scale domain feature aggregation is proposed to address the interference of complex background. Second, bidirectional embedding fusion adaptive attention is proposed to capture the features of tiny cracks, and finally, Gaussian weighted edge segmentation algorithm is proposed to ensure the accuracy of crack edge segmentation. In addition, this paper uses the preheated bat optimization algorithm, which can quickly determine the optimal learning rate to converge the equilibrium. In the validation experiments on the self-built dataset, mean intersection over union reaches 80.54% and precision of 86.38%. MBGBNet outperforms the other seven state-of-the-art crack segmentation networks on the three classical crack datasets, highlighting its advanced segmentation capabilities. Therefore, MBGBNet is an effective auxiliary method for solving road safety problems.
{"title":"An optimized and precise road crack segmentation network in complex scenarios","authors":"Gang Wang, MingFang He, Genhua Liu, Liujun Li, Exian Liu, Guoxiong Zhou","doi":"10.1111/mice.13444","DOIUrl":"https://doi.org/10.1111/mice.13444","url":null,"abstract":"Road cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi-scale domain feature aggregation is proposed to address the interference of complex background. Second, bidirectional embedding fusion adaptive attention is proposed to capture the features of tiny cracks, and finally, Gaussian weighted edge segmentation algorithm is proposed to ensure the accuracy of crack edge segmentation. In addition, this paper uses the preheated bat optimization algorithm, which can quickly determine the optimal learning rate to converge the equilibrium. In the validation experiments on the self-built dataset, mean intersection over union reaches 80.54% and precision of 86.38%. MBGBNet outperforms the other seven state-of-the-art crack segmentation networks on the three classical crack datasets, highlighting its advanced segmentation capabilities. Therefore, MBGBNet is an effective auxiliary method for solving road safety problems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"52 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427302","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}
Semantic segmentation struggles with detecting undefined road obstacles, critical for autonomous driving in urban environments. This study addresses the need for accurate unknown obstacle detection, inspired by drivers’ instinctual vigilance toward unexpected objects. It explores the impact of unexpected object position patterns on anomaly detection using human fixation scan‐paths and gaze density heat maps. Data augmentation based on these patterns enhances the outlier dataset for anomaly detection networks. The proposed driving vigilance enhancement framework (DVEF) improves classification accuracy with multi‐scale detailed features and a vigilance enhancement model, generating vigilance score maps to prioritize unknown regions. An improved energy model joint loss function expands vigilance scores, enhancing anomaly detection accuracy. Compared with recent methods on Fishyscapes (FS) LostAndFound, FS Static, and average datasets, average precision improvements of 2.16%, 2.22%, and 2.89% are achieved on these datasets, respectively. In addition, the false positive rate at a true positive rate of 95% are decreased to 5.79%, 5.62%, and 17.89%, respectively. It is indicated that the performance of the encoder–decoder semantic segmentation network is improved by DVEF, with enhanced consistency and robustness.
{"title":"Pixel‐wise anomaly detection on road by encoder–decoder semantic segmentation framework with driving vigilance","authors":"Yipeng Liu, Jianqing Wu, Xiuguang Song","doi":"10.1111/mice.13443","DOIUrl":"https://doi.org/10.1111/mice.13443","url":null,"abstract":"Semantic segmentation struggles with detecting undefined road obstacles, critical for autonomous driving in urban environments. This study addresses the need for accurate unknown obstacle detection, inspired by drivers’ instinctual vigilance toward unexpected objects. It explores the impact of unexpected object position patterns on anomaly detection using human fixation scan‐paths and gaze density heat maps. Data augmentation based on these patterns enhances the outlier dataset for anomaly detection networks. The proposed driving vigilance enhancement framework (DVEF) improves classification accuracy with multi‐scale detailed features and a vigilance enhancement model, generating vigilance score maps to prioritize unknown regions. An improved energy model joint loss function expands vigilance scores, enhancing anomaly detection accuracy. Compared with recent methods on Fishyscapes (FS) LostAndFound, FS Static, and average datasets, average precision improvements of 2.16%, 2.22%, and 2.89% are achieved on these datasets, respectively. In addition, the false positive rate at a true positive rate of 95% are decreased to 5.79%, 5.62%, and 17.89%, respectively. It is indicated that the performance of the encoder–decoder semantic segmentation network is improved by DVEF, with enhanced consistency and robustness.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"33 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417506","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 Optimizing green splits in high-dimensional traffic signal control with trust region Bayesian optimization by Yu Jiang et al., https://doi.org/10.1111/mice.13293.
{"title":"Cover Image, Volume 40, Issue 6","authors":"","doi":"10.1111/mice.13441","DOIUrl":"https://doi.org/10.1111/mice.13441","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Optimizing green splits in high-dimensional traffic signal control with trust region Bayesian optimization</i> by Yu Jiang et al., https://doi.org/10.1111/mice.13293.\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 6","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404440","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}
Haowei Zhang, Kang Gao, Yue Hou, Marco Domaneschi, Mohammad Noori
The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms.
{"title":"Portable IoT device for tire text code identification via integrated computer vision system","authors":"Haowei Zhang, Kang Gao, Yue Hou, Marco Domaneschi, Mohammad Noori","doi":"10.1111/mice.13438","DOIUrl":"https://doi.org/10.1111/mice.13438","url":null,"abstract":"The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401935","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 structure-oriented loss function for automated semantic segmentation of bridge point cloudsc by Pang-jo Chun et al., https://doi.org/10.1111/mice.13422.
{"title":"Cover Image, Volume 40, Issue 6","authors":"","doi":"10.1111/mice.13442","DOIUrl":"https://doi.org/10.1111/mice.13442","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>A structure-oriented loss function for automated semantic segmentation of bridge point cloudsc</i> by Pang-jo Chun et al., https://doi.org/10.1111/mice.13422.\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 6","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404441","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}
Yongjiang Chen, Kui Wang, Mingjie Zhao, JianFeng Liu
The reconstruction of monitoring data reconstruction is an important step in the process of structural health monitoring. Monitoring data reconstruction involves generating values that are close to the true or expected values, and then using the generated values to replace the anomalous data or fill in the missing data. Deep learning models can be used to reconstruct dam monitoring data, but current models suffer from the inabilities to reconstruct data when the dataset is significantly incomplete, and the reconstruction accuracy and speed have needs for improvement. To this end, this paper proposes a dam temporal reconstruction nets (DTRN) based on generative adversarial nets, which is used to accurately reconstruct dam monitoring data for cases of incomplete datasets. To improve the accuracy of the reconstruction values, this paper embeds a gated recurrent unit network based on a sequence-to-sequence model into DTRN to extract the temporal features of the dam monitoring data. In addition, given that random matrices with different distributions lead to different reconstruction results, maximum probability reconstruction based on multiple filling is adopted. Finally, several experiments show that (1) DTRN is not only applicable to the reconstruction of various types of dam monitoring data (e.g., dam displacement monitoring data, dam seepage pressure monitoring data, seam gauge monitoring data, etc.) but also can be applied to other relatively smooth time series data. (2) The average root mean square error of DTRN (0.0618) indicates that its accuracy is 92.3%, 57.5%, and 71.99% higher than that of generative adversarial imputation nets (GAIN), timing GAIN (TGAIN), and dam monitoring data reconstruction network (DMDRN), respectively. (3) The average elapsed time of DTRN (522.6 s) is 68.45% and 48.10% shorter than that of TGAIN and DMDRN, respectively.
{"title":"Network models for temporal data reconstruction for dam health monitoring","authors":"Yongjiang Chen, Kui Wang, Mingjie Zhao, JianFeng Liu","doi":"10.1111/mice.13431","DOIUrl":"https://doi.org/10.1111/mice.13431","url":null,"abstract":"The reconstruction of monitoring data reconstruction is an important step in the process of structural health monitoring. Monitoring data reconstruction involves generating values that are close to the true or expected values, and then using the generated values to replace the anomalous data or fill in the missing data. Deep learning models can be used to reconstruct dam monitoring data, but current models suffer from the inabilities to reconstruct data when the dataset is significantly incomplete, and the reconstruction accuracy and speed have needs for improvement. To this end, this paper proposes a dam temporal reconstruction nets (DTRN) based on generative adversarial nets, which is used to accurately reconstruct dam monitoring data for cases of incomplete datasets. To improve the accuracy of the reconstruction values, this paper embeds a gated recurrent unit network based on a sequence-to-sequence model into DTRN to extract the temporal features of the dam monitoring data. In addition, given that random matrices with different distributions lead to different reconstruction results, maximum probability reconstruction based on multiple filling is adopted. Finally, several experiments show that (1) DTRN is not only applicable to the reconstruction of various types of dam monitoring data (e.g., dam displacement monitoring data, dam seepage pressure monitoring data, seam gauge monitoring data, etc.) but also can be applied to other relatively smooth time series data. (2) The average root mean square error of DTRN (0.0618) indicates that its accuracy is 92.3%, 57.5%, and 71.99% higher than that of generative adversarial imputation nets (GAIN), timing GAIN (TGAIN), and dam monitoring data reconstruction network (DMDRN), respectively. (3) The average elapsed time of DTRN (522.6 s) is 68.45% and 48.10% shorter than that of TGAIN and DMDRN, respectively.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"128 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401936","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}
Asya Atik, Kuangying Li, Leila Hajibabai, Ali Hajbabaie
This study develops an integrated delivery assignment and route planning strategy for food banking operations, considering food supply and demand constraints, food item restrictions, and vehicle capacity constraints. A mixed-integer linear model is formulated to maximize the total demand served and minimize the total travel cost imposed on delivery volunteers. An integrated solution algorithm is developed that includes Lagrangian relaxation and column generation. The algorithm decomposes the problem into assignment and routing components and solves each iteratively. The proposed methodology is applied to a case study in Wake County, NC. A series of sensitivity analyses are conducted to draw insights. The numerical results demonstrate the proposed methodology's capacity to solve complex problems in food delivery operations efficiently.
{"title":"Integrated column generation for volunteer-based delivery assignment and route optimization","authors":"Asya Atik, Kuangying Li, Leila Hajibabai, Ali Hajbabaie","doi":"10.1111/mice.13439","DOIUrl":"https://doi.org/10.1111/mice.13439","url":null,"abstract":"This study develops an integrated delivery assignment and route planning strategy for food banking operations, considering food supply and demand constraints, food item restrictions, and vehicle capacity constraints. A mixed-integer linear model is formulated to maximize the total demand served and minimize the total travel cost imposed on delivery volunteers. An integrated solution algorithm is developed that includes Lagrangian relaxation and column generation. The algorithm decomposes the problem into assignment and routing components and solves each iteratively. The proposed methodology is applied to a case study in Wake County, NC. A series of sensitivity analyses are conducted to draw insights. The numerical results demonstrate the proposed methodology's capacity to solve complex problems in food delivery operations efficiently.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"2 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393705","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}
Salient object detection (SOD) is a crucial preprocessing technique in visual computing, which identifies the salient regions in an image by simulating the human visual perception system. It achieves remarkable results in tasks such as image quality assessment, editing, and object recognition. However, due to the particularity of pavement crack detection in terms of scale and feature requirements, the SOD model is rarely applied in pavement surface crack detection at present. In order to break the existing dilemma, this paper proposes a new SOD model (iU2Net) specialized for crack detection, which is based on the encoder–decoder structure of U2Net and incorporates the developed interactive cross-multi-feature fusion module (ICMFM). Compared with the existing models, the main contributions of iU2Net are reflected in two aspects. On the one hand, current models are difficult to comprehensively extract the complex features of cracks while iU2Net achieves a breakthrough in feature extraction by efficiently aggregating multiscale crack features and accurately reconstructing them through its unique architecture. On the other hand, iU2Net focuses on infrastructure crack detection, breaking the limitation of independent processing of traditional feature channels and facilitating information exchange. To validate the model's effectiveness, comprehensive experiments are conducted on a public benchmark dataset. iU2Net is compared with eight existing SOD models (EGNet, PoolNet, MINet, F3Net, U2Net, SegNet, BASNet, and DeepCrack). Training and detection performance is evaluated using average mean absolute error (AveMAE), maximum F1 score (MaxF1), mean F1 score (MeanF1), precision–recall curves, and visualizations. Experimental the results indicate that iU2Net exceeds the behavior of other networks during both the training and testing phases, with MaxF1 and MeanF1 achieving maximum values of 0.912 and 0.730, respectively; and AveMAE of 0.048, which is only 0.005 higher than the minimum value, which demonstrates its effectiveness for pavement surface crack detection and indicating potential future applications involving interactive feature fusion.
{"title":"An interactive cross-multi-feature fusion approach for salient object detection in crack segmentation","authors":"Jian Liu, Pei Niu, Lei Kou, Yalin Zhang, Honglei Chang, Feng Guo","doi":"10.1111/mice.13437","DOIUrl":"https://doi.org/10.1111/mice.13437","url":null,"abstract":"Salient object detection (SOD) is a crucial preprocessing technique in visual computing, which identifies the salient regions in an image by simulating the human visual perception system. It achieves remarkable results in tasks such as image quality assessment, editing, and object recognition. However, due to the particularity of pavement crack detection in terms of scale and feature requirements, the SOD model is rarely applied in pavement surface crack detection at present. In order to break the existing dilemma, this paper proposes a new SOD model (iU2Net) specialized for crack detection, which is based on the encoder–decoder structure of U2Net and incorporates the developed interactive cross-multi-feature fusion module (ICMFM). Compared with the existing models, the main contributions of iU2Net are reflected in two aspects. On the one hand, current models are difficult to comprehensively extract the complex features of cracks while iU2Net achieves a breakthrough in feature extraction by efficiently aggregating multiscale crack features and accurately reconstructing them through its unique architecture. On the other hand, iU2Net focuses on infrastructure crack detection, breaking the limitation of independent processing of traditional feature channels and facilitating information exchange. To validate the model's effectiveness, comprehensive experiments are conducted on a public benchmark dataset. iU2Net is compared with eight existing SOD models (EGNet, PoolNet, MINet, F3Net, U2Net, SegNet, BASNet, and DeepCrack). Training and detection performance is evaluated using average mean absolute error (AveMAE), maximum F1 score (MaxF1), mean F1 score (MeanF1), precision–recall curves, and visualizations. Experimental the results indicate that iU2Net exceeds the behavior of other networks during both the training and testing phases, with MaxF1 and MeanF1 achieving maximum values of 0.912 and 0.730, respectively; and AveMAE of 0.048, which is only 0.005 higher than the minimum value, which demonstrates its effectiveness for pavement surface crack detection and indicating potential future applications involving interactive feature fusion.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"10 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375474","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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