Pore structure characteristics of cementitious materials play a critical role in the transport properties of concrete structures. This paper develops a novel framework for modeling chloride penetration in concrete, considering the pore structure‐dependent model parameters. In the framework, a multi‐scale transport model was derived by linking the chloride diffusivities with pore size distributions (PSDs). Based on the three‐dimensional (3D) microstructure generated by “porous growth” and “hard core‐soft shell” methods, two sub‐models were computationally developed for determining the multi‐modal PSDs and pore size‐related chloride diffusivities. The predicted results by these series of models were compared with corresponding experimental data. The results indicated that by adopting pore size‐related diffusivities, even if the total porosities were the same, the proposed multi‐scale chloride transport model could better capture the effect of different PSDs on chloride penetration profiles, while the model without pore structure‐depended parameters would ignore the differences. Compared with the reference transport models, which adopt averaged chloride diffusivities, the chloride penetration depths predicted by the proposed multi‐scale model are in better agreement with experimental data, with 10%–25% reduced prediction error. This multi‐scale transport model is hoped to provide a novel computational approach on 3D microstructure generation and better reveal the underlying mechanism of the chloride penetration process in concrete from a microscopic perspective.
{"title":"Modeling the chloride transport in concrete from microstructure generation to chloride diffusivity prediction","authors":"Liang‐yu Tong, Qing‐feng Liu, Qingxiang Xiong, Zhaozheng Meng, Ouali Amiri, Mingzhong Zhang","doi":"10.1111/mice.13331","DOIUrl":"https://doi.org/10.1111/mice.13331","url":null,"abstract":"Pore structure characteristics of cementitious materials play a critical role in the transport properties of concrete structures. This paper develops a novel framework for modeling chloride penetration in concrete, considering the pore structure‐dependent model parameters. In the framework, a multi‐scale transport model was derived by linking the chloride diffusivities with pore size distributions (PSDs). Based on the three‐dimensional (3D) microstructure generated by “porous growth” and “hard core‐soft shell” methods, two sub‐models were computationally developed for determining the multi‐modal PSDs and pore size‐related chloride diffusivities. The predicted results by these series of models were compared with corresponding experimental data. The results indicated that by adopting pore size‐related diffusivities, even if the total porosities were the same, the proposed multi‐scale chloride transport model could better capture the effect of different PSDs on chloride penetration profiles, while the model without pore structure‐depended parameters would ignore the differences. Compared with the reference transport models, which adopt averaged chloride diffusivities, the chloride penetration depths predicted by the proposed multi‐scale model are in better agreement with experimental data, with 10%–25% reduced prediction error. This multi‐scale transport model is hoped to provide a novel computational approach on 3D microstructure generation and better reveal the underlying mechanism of the chloride penetration process in concrete from a microscopic perspective.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"29 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101213","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}
Sina Tavasoli, Sina Poorghasem, Xiao Pan, T. Y. Yang, Y. Bao
This paper introduces an innovative autonomous survivor detection pipeline tailored for low‐cost micro aerial vehicles (MAVs) operating in post‐disaster indoor environments. This consists of three main components: (1) a novel pipeline for survivor geotagging, which includes autonomous navigation, mapping, and detection of survivors using thermal images; (2) a navigation strategy to ensure complete thermal scanning coverage for survivor detection using low‐cost commercial grade thermal camera; and (3) robust and accurate survivor detection using YOLOv8x and thermal imaging. To demonstrate the effectiveness of the proposed framework, first, the autonomous navigation algorithm is simulated in Robotic Operating System (ROS) and experimentally validated under different layouts. Second, the YOLOv8x algorithm is pretrained and achieves high accuracy. Finally, a real‐world implementation was conducted with partially covered survivors in a simulated post‐disaster environment. The results demonstrated the proposed pipeline can accurately map the layout of the environment and identify all survivors. This study demonstrates that affordable MAVs with basic thermal cameras can be effectively used to geotag survivors to support rescue missions during post‐disaster events.
{"title":"Autonomous post‐disaster indoor navigation and survivor detection using low‐cost micro aerial vehicles","authors":"Sina Tavasoli, Sina Poorghasem, Xiao Pan, T. Y. Yang, Y. Bao","doi":"10.1111/mice.13319","DOIUrl":"https://doi.org/10.1111/mice.13319","url":null,"abstract":"This paper introduces an innovative autonomous survivor detection pipeline tailored for low‐cost micro aerial vehicles (MAVs) operating in post‐disaster indoor environments. This consists of three main components: (1) a novel pipeline for survivor geotagging, which includes autonomous navigation, mapping, and detection of survivors using thermal images; (2) a navigation strategy to ensure complete thermal scanning coverage for survivor detection using low‐cost commercial grade thermal camera; and (3) robust and accurate survivor detection using YOLOv8x and thermal imaging. To demonstrate the effectiveness of the proposed framework, first, the autonomous navigation algorithm is simulated in Robotic Operating System (ROS) and experimentally validated under different layouts. Second, the YOLOv8x algorithm is pretrained and achieves high accuracy. Finally, a real‐world implementation was conducted with partially covered survivors in a simulated post‐disaster environment. The results demonstrated the proposed pipeline can accurately map the layout of the environment and identify all survivors. This study demonstrates that affordable MAVs with basic thermal cameras can be effectively used to geotag survivors to support rescue missions during post‐disaster events.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"51 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085434","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}
Building change detection (BCD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built‐up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR‐Net) to improve the accuracy and robustness of BCD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR‐Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR‐Net outperforms state‐of‐the‐art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.
{"title":"A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images","authors":"Zhihuan Liu, Zaichun Yang, Tingting Ren, Zhenzhen Wang, JinSheng Deng, Chenxi Deng, Hongmin Zhao, Guoxiong Zhou, Aibin Chen, Liujun Li","doi":"10.1111/mice.13330","DOIUrl":"https://doi.org/10.1111/mice.13330","url":null,"abstract":"Building change detection (BCD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built‐up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR‐Net) to improve the accuracy and robustness of BCD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR‐Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR‐Net outperforms state‐of‐the‐art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085134","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. L. Gu, Q. W. Shuai, N. Zhang, N. Jin, Z. X. Zheng, Z. Xu, Y. J. Xu
Global warming amplifies the risk of wind‐induced building damage in coastal cities worldwide. Existing numerical methods for predicting building damage under winds have been limited to virtual environments, given the prohibitive costs associated with establishing city‐scale window inventories. Hence, this study introduces a cost‐effective workflow for wind damage prediction of real built environments, where the window inventory can be established with the multi‐view street view image (SVI) fusion and artificial intelligence large model. The feasibility of the method is demonstrated based on two real‐world urban areas. Notably, the proposed multi‐view method surpasses both the single‐view and aerial image‐based methods in terms of window recognition accuracy. The increasing availability of SVIs opens up opportunities for applying the proposed method not only in disaster prevention but also in environmental and energy topics, thereby enhancing the resilience of cities and communities from multiple perspectives.
{"title":"Multi‐view street view image fusion for city‐scale assessment of wind damage to building clusters","authors":"D. L. Gu, Q. W. Shuai, N. Zhang, N. Jin, Z. X. Zheng, Z. Xu, Y. J. Xu","doi":"10.1111/mice.13324","DOIUrl":"https://doi.org/10.1111/mice.13324","url":null,"abstract":"Global warming amplifies the risk of wind‐induced building damage in coastal cities worldwide. Existing numerical methods for predicting building damage under winds have been limited to virtual environments, given the prohibitive costs associated with establishing city‐scale window inventories. Hence, this study introduces a cost‐effective workflow for wind damage prediction of real built environments, where the window inventory can be established with the multi‐view street view image (SVI) fusion and artificial intelligence large model. The feasibility of the method is demonstrated based on two real‐world urban areas. Notably, the proposed multi‐view method surpasses both the single‐view and aerial image‐based methods in terms of window recognition accuracy. The increasing availability of SVIs opens up opportunities for applying the proposed method not only in disaster prevention but also in environmental and energy topics, thereby enhancing the resilience of cities and communities from multiple perspectives.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"58 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045461","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 high-rise residential shear wall structure is a crucial component of urban building clusters, while the limited availability of detailed structural information becomes a critical bottleneck in improving the accuracy of seismic performance assessment for high-rise residential shear wall buildings in urban areas. Based on easily obtainable yet limited structural data at the urban scale, this paper proposes a method to address the shortcomings of existing research on reconstructing hidden structural information and enhance the accuracy of structural seismic performance assessment. It includes a physics-constrained generative adversarial network module and a fuzzy inference system module to reconstruct the spatial arrangement of shear walls, and material strength grades within buildings, respectively. Validated against two actual buildings, the method outperforms the widely used simplified analysis method at the urban scale, achieving 85.9% accuracy in predicting damage states across various floors.
{"title":"Hidden structural information reconstruction and seismic response analysis of high-rise residential shear wall buildings with limited structural data","authors":"Chenyu Zhang, Weiping Wen, Changhai Zhai, Yuqiu Wei, Penghao Ruan","doi":"10.1111/mice.13320","DOIUrl":"https://doi.org/10.1111/mice.13320","url":null,"abstract":"The high-rise residential shear wall structure is a crucial component of urban building clusters, while the limited availability of detailed structural information becomes a critical bottleneck in improving the accuracy of seismic performance assessment for high-rise residential shear wall buildings in urban areas. Based on easily obtainable yet limited structural data at the urban scale, this paper proposes a method to address the shortcomings of existing research on reconstructing hidden structural information and enhance the accuracy of structural seismic performance assessment. It includes a physics-constrained generative adversarial network module and a fuzzy inference system module to reconstruct the spatial arrangement of shear walls, and material strength grades within buildings, respectively. Validated against two actual buildings, the method outperforms the widely used simplified analysis method at the urban scale, achieving 85.9% accuracy in predicting damage states across various floors.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"2 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142023077","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 Machine learning-informed soil conditioning for mechanized shield tunneling by Shuying Wang et al., https://doi.org/10.1111/mice.13152.
{"title":"Cover Image, Volume 39, Issue 17","authors":"","doi":"10.1111/mice.13327","DOIUrl":"https://doi.org/10.1111/mice.13327","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>Machine learning-informed soil conditioning for mechanized shield tunneling</i> by Shuying Wang et al., https://doi.org/10.1111/mice.13152.\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":"39 17","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994163","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}
Prestressing wire breakage induced by corrosion is hazardous, especially for concrete structures subjected to severe aging factors, such as bridges. Developing an automated monitoring system for such a damage event is therefore essential for ensuring structural integrity and preventing catastrophic failures. In line with this target, a supervised deep learning–based approach is proposed to detect and classify acoustic emissions released by prestressing wire breakage. The application of advanced signal processing techniques is central to this study to determine optimal model performance and accurately detect patterns of various events. Diverse pretrained convolutional neural network (CNN) architectures are explored and further enhanced by incorporating Bottleneck Attention Mechanisms to refine their performance capabilities. Additionally, a novel hybrid model, AcousticNet, tailored for acoustic event classification in the context of structural health monitoring, is developed. The models are trained and validated using an extensive data set collected from controlled laboratory experiments and in situ bridge monitoring scenarios, ensuring comprehensive adaptability and generalizability. The comprehensive analysis highlights that the Xception model, enhanced with a bottleneck module, and AcousticNet significantly outperform other models in capturing intricate patterns within acoustic signals. Integrating advanced CNN architectures with signal processing methods marks a substantial advancement in the automated monitoring of prestressed concrete bridges.
{"title":"Automated acoustic event‐based monitoring of prestressing tendons breakage in concrete bridges","authors":"Sasan Farhadi, Mauro Corrado, Giulio Ventura","doi":"10.1111/mice.13321","DOIUrl":"https://doi.org/10.1111/mice.13321","url":null,"abstract":"Prestressing wire breakage induced by corrosion is hazardous, especially for concrete structures subjected to severe aging factors, such as bridges. Developing an automated monitoring system for such a damage event is therefore essential for ensuring structural integrity and preventing catastrophic failures. In line with this target, a supervised deep learning–based approach is proposed to detect and classify acoustic emissions released by prestressing wire breakage. The application of advanced signal processing techniques is central to this study to determine optimal model performance and accurately detect patterns of various events. Diverse pretrained convolutional neural network (CNN) architectures are explored and further enhanced by incorporating Bottleneck Attention Mechanisms to refine their performance capabilities. Additionally, a novel hybrid model, AcousticNet, tailored for acoustic event classification in the context of structural health monitoring, is developed. The models are trained and validated using an extensive data set collected from controlled laboratory experiments and in situ bridge monitoring scenarios, ensuring comprehensive adaptability and generalizability. The comprehensive analysis highlights that the Xception model, enhanced with a bottleneck module, and AcousticNet significantly outperform other models in capturing intricate patterns within acoustic signals. Integrating advanced CNN architectures with signal processing methods marks a substantial advancement in the automated monitoring of prestressed concrete bridges.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998704","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}
Mohammadjavad Berangi, Bernardo Mota Lontra, Kumar Anupam, Sandra Erkens, Dave Van Vliet, Almar Snippe, Mahesh Moenielal
Inconsistencies between performance data from laboratory‐prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self‐learning systems that could quickly transfer laboratory‐based information to field‐based information inside the PMS. The article aims to present a future‐ready machine learning‐based framework for analyzing the differences between laboratory and field‐prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient‐boosting decision trees‐based framework was able to establish a good relationship between laboratory performance and field performance (R2test > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice.
{"title":"Gradient boosting decision trees to study laboratory and field performance in pavement management","authors":"Mohammadjavad Berangi, Bernardo Mota Lontra, Kumar Anupam, Sandra Erkens, Dave Van Vliet, Almar Snippe, Mahesh Moenielal","doi":"10.1111/mice.13322","DOIUrl":"https://doi.org/10.1111/mice.13322","url":null,"abstract":"Inconsistencies between performance data from laboratory‐prepared and field samples have been widely reported. These inconsistencies often result in inaccurate condition prediction, which leads to inefficient maintenance planning. Traditional pavement management systems (PMS) do not have the appropriate means (e.g., mechanistic solutions, extensive data handling facilities, etc.) to consider these data inconsistencies. With the growing demand for sustainable materials, there is a need for more self‐learning systems that could quickly transfer laboratory‐based information to field‐based information inside the PMS. The article aims to present a future‐ready machine learning‐based framework for analyzing the differences between laboratory and field‐prepared samples. Developed on the basis of data obtained from field and laboratory data, the gradient‐boosting decision trees‐based framework was able to establish a good relationship between laboratory performance and field performance (<jats:italic>R</jats:italic><jats:sup>2</jats:sup><jats:sub>test</jats:sub> > 80 for all models). At the same time, the framework could also show more complex relationships that are often not considered in practice.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"96 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998705","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 Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation by Pang-jo Chun and Toshiya Kikuta, https://doi.org/10.1111/mice.13315.
封面图像基于 Pang-jo Chun 和 Toshiya Kikuta 的文章《裂缝分割中无监督域适应的贝叶斯神经网络和空间先验的自我训练》（Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation），https://doi.org/10.1111/mice.13315。
{"title":"Cover Image, Volume 39, Issue 17","authors":"","doi":"10.1111/mice.13328","DOIUrl":"https://doi.org/10.1111/mice.13328","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation</i> by Pang-jo Chun and Toshiya Kikuta, https://doi.org/10.1111/mice.13315.\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":"39 17","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994157","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}
Predicting stratified ground consolidation effectively remains a challenge in geotechnical engineering, especially when it comes to quickly and dependably determining the coefficient of consolidation () for each soil layer. This difficulty primarily stems from the time‐intensive nature of the consolidation process and the challenges in efficiently simulating this process in laboratory settings and using numerical methods. Nevertheless, the consolidation of stratified ground is crucial because it governs ground settlement, affecting the safety and serviceability of structures situated on or in such ground. In this study, an innovative method utilizing a physics‐informed neural network (PINN) is introduced to predict stratified ground consolidation, relying solely on short‐term excess pore water pressure (PWP) data collected by monitoring sensors. The proposed PINN framework identifies from the limited PWP data set and subsequently utilizes the identified to predict the long‐term consolidation process of stratified ground. The efficacy of the method is demonstrated through its application to a case study involving two‐layer ground consolidation, with comparisons made to an existing PINN method and a laboratory consolidation test. The results of the case study demonstrate the applicability of the proposed PINN method to both forward and inverse consolidation problems. Specifically, the method accurately predicts the long‐term dissipation of excess PWP when is known (i.e., the forward problem). It successfully identifies the unknown with only 0.05‐year monitoring data comprising 10 data points and predicts the dissipation of excess PWP at 1‐year, 10‐year, 15‐year, and even up to 30‐year intervals using the identified (i.e., the inverse problem). Moreover, the investigation into optimal PWP monitoring sensor layouts reveals that installing sensors in areas with significant variations in excess PWP enhances the prediction accuracy of the proposed PINN method. The results underscore the potential of leveraging PINNs in conjunction with PWP monitoring sensors to effectively predict stratified ground consolidation.
{"title":"Prediction of stratified ground consolidation via a physics‐informed neural network utilizing short‐term excess pore water pressure monitoring data","authors":"Weibing Gong, Linlong Zuo, Lin Li, Hui Wang","doi":"10.1111/mice.13326","DOIUrl":"https://doi.org/10.1111/mice.13326","url":null,"abstract":"Predicting stratified ground consolidation effectively remains a challenge in geotechnical engineering, especially when it comes to quickly and dependably determining the coefficient of consolidation () for each soil layer. This difficulty primarily stems from the time‐intensive nature of the consolidation process and the challenges in efficiently simulating this process in laboratory settings and using numerical methods. Nevertheless, the consolidation of stratified ground is crucial because it governs ground settlement, affecting the safety and serviceability of structures situated on or in such ground. In this study, an innovative method utilizing a physics‐informed neural network (PINN) is introduced to predict stratified ground consolidation, relying solely on short‐term excess pore water pressure (PWP) data collected by monitoring sensors. The proposed PINN framework identifies from the limited PWP data set and subsequently utilizes the identified to predict the long‐term consolidation process of stratified ground. The efficacy of the method is demonstrated through its application to a case study involving two‐layer ground consolidation, with comparisons made to an existing PINN method and a laboratory consolidation test. The results of the case study demonstrate the applicability of the proposed PINN method to both forward and inverse consolidation problems. Specifically, the method accurately predicts the long‐term dissipation of excess PWP when is known (i.e., the forward problem). It successfully identifies the unknown with only 0.05‐year monitoring data comprising 10 data points and predicts the dissipation of excess PWP at 1‐year, 10‐year, 15‐year, and even up to 30‐year intervals using the identified (i.e., the inverse problem). Moreover, the investigation into optimal PWP monitoring sensor layouts reveals that installing sensors in areas with significant variations in excess PWP enhances the prediction accuracy of the proposed PINN method. The results underscore the potential of leveraging PINNs in conjunction with PWP monitoring sensors to effectively predict stratified ground consolidation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"11 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998728","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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