The cover image is based on the Research Article Deep learning-based automatic classification of three-level surface information in bridge inspection by He Zhang et al., https://doi.org/10.1111/mice.13117.
{"title":"Cover Image, Volume 39, Issue 10","authors":"","doi":"10.1111/mice.13220","DOIUrl":"https://doi.org/10.1111/mice.13220","url":null,"abstract":"<p><b>The cover image</b> is based on the Research Article <i>Deep learning-based automatic classification of three-level surface information in bridge inspection</i> by He Zhang et al., https://doi.org/10.1111/mice.13117.\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":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819003","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}
Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category are reconstructed. Then, the reference alignment category to which the track to be adjusted belongs is accurately matched. Finally, a novel smoothness optimization algorithm is designed to use the 60 m chord as the optimization unit, and the 10 m and 30 m combined chords within the unit constrain the midchord offset and vector distance difference. The proposed method was applied to formulate the maintenance scheme for the Shanghai–Suzhou–Nantong Yangtze River Bridge. The result indicates that the track smoothness improved by more than 79.7%, and the high‐speed train operational performance improved by over 64.3%, effectively enhancing the maintenance quality.
{"title":"A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics","authors":"Yuxiao Zhang, Jin Shi, Shehui Tan, Yingjie Wang","doi":"10.1111/mice.13215","DOIUrl":"https://doi.org/10.1111/mice.13215","url":null,"abstract":"Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category are reconstructed. Then, the reference alignment category to which the track to be adjusted belongs is accurately matched. Finally, a novel smoothness optimization algorithm is designed to use the 60 m chord as the optimization unit, and the 10 m and 30 m combined chords within the unit constrain the midchord offset and vector distance difference. The proposed method was applied to formulate the maintenance scheme for the Shanghai–Suzhou–Nantong Yangtze River Bridge. The result indicates that the track smoothness improved by more than 79.7%, and the high‐speed train operational performance improved by over 64.3%, effectively enhancing the maintenance quality.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818060","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}
Hao Huang, Jiannan Mao, Leilei Kang, Weike Lu, Sijia Zhang, Lan Liu
Network-wide short-term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non-stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi-scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically, the model employs multivariate empirical mode decomposition to jointly decompose the multivariate passenger flow into multi-scale intrinsic mode functions. Then, a set of dynamic graphs is developed to reveal the passenger propagation law in metro networks. Based on the representation, a deep learning model is proposed to achieve multistep passenger flow prediction, which employs the dynamic propagation graph attention network with long short-term memory to extract the spatial–temporal dependencies. Extensive experiments conducted on a real-world dataset from Chengdu, China, validate the superiority of the proposed model. Compared to state-of-the-art baselines, MSDPSTN reduces the mean absolute error, root mean squared error, and mean absolute percentage error by at least 3.243%, 4.451%, and 4.139%, respectively. Further quantitative analyses confirm the effectiveness of the components in MSDPSTN. This paper contributes to addressing inherent features of passenger flow to enhance prediction performance, offering critical insights for decision-makers in implementing real-time operational strategies.
{"title":"A dynamic graph deep learning model with multivariate empirical mode decomposition for network-wide metro passenger flow prediction","authors":"Hao Huang, Jiannan Mao, Leilei Kang, Weike Lu, Sijia Zhang, Lan Liu","doi":"10.1111/mice.13214","DOIUrl":"https://doi.org/10.1111/mice.13214","url":null,"abstract":"Network-wide short-term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non-stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi-scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically, the model employs multivariate empirical mode decomposition to jointly decompose the multivariate passenger flow into multi-scale intrinsic mode functions. Then, a set of dynamic graphs is developed to reveal the passenger propagation law in metro networks. Based on the representation, a deep learning model is proposed to achieve multistep passenger flow prediction, which employs the dynamic propagation graph attention network with long short-term memory to extract the spatial–temporal dependencies. Extensive experiments conducted on a real-world dataset from Chengdu, China, validate the superiority of the proposed model. Compared to state-of-the-art baselines, MSDPSTN reduces the mean absolute error, root mean squared error, and mean absolute percentage error by at least 3.243%, 4.451%, and 4.139%, respectively. Further quantitative analyses confirm the effectiveness of the components in MSDPSTN. This paper contributes to addressing inherent features of passenger flow to enhance prediction performance, offering critical insights for decision-makers in implementing real-time operational strategies.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814996","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}
Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three-dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component-wise learning combining the initial weight transfer, to overcome the difficulty particularly found in the bridge structures, where a whole structure consists of multiple and various components. The learning method was developed and verified using point cloud data acquired in an actual concrete bridge based on the point cloud completion performance of three significant deep learning models. The effectiveness and applicability of the proposed method were shown in that it improved performances in component level in applying it to the bridge point cloud completion by the multiple deep learning models, respectively.
{"title":"Component-level point cloud completion of bridge structures using deep learning","authors":"Gen Matono, Mayuko Nishio","doi":"10.1111/mice.13218","DOIUrl":"https://doi.org/10.1111/mice.13218","url":null,"abstract":"Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three-dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component-wise learning combining the initial weight transfer, to overcome the difficulty particularly found in the bridge structures, where a whole structure consists of multiple and various components. The learning method was developed and verified using point cloud data acquired in an actual concrete bridge based on the point cloud completion performance of three significant deep learning models. The effectiveness and applicability of the proposed method were shown in that it improved performances in component level in applying it to the bridge point cloud completion by the multiple deep learning models, respectively.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140651509","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 multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large-scale model and inherent solution symmetry associated with the traditional trip-to-trip connection-based approach for MDVSP, a new trip-to-route (T2R) connection-based approach is proposed. Considering real-world problem characteristics with numerous trips sharing common origin–destination stations and travel times on one route, this approach aggregates same vehicle possible trip sequences into a T2R connection. Two time-space network aggregation (TSNA) flow formulation versions, route pair-based TSNA and station pair-based TSNA, were constructed. Furthermore, TSNA equivalence under any given decomposition strategy, including first-in-first-out, with the multicommodity network flow (MCNF) model was demonstrated. Given the favorable separable TSNA structure, an alternating direction method of multipliers (ADMM)-based procedure is proposed to decompose the MDVSP into multiple subproblems that can be linearized and readily solved using commercial solvers. The quality of the solutions was assessed using lower bounds obtained from the Lagrangian relaxation problem. The effectiveness and superiority of the proposed MDVSP models and algorithms were subsequently confirmed using random data sets and real-world instances.
{"title":"Aggregation formulation for on-site multidepot vehicle scheduling scenario","authors":"Yi Gao, Yuanjie Tang, Rengkui Liu","doi":"10.1111/mice.13217","DOIUrl":"https://doi.org/10.1111/mice.13217","url":null,"abstract":"The multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large-scale model and inherent solution symmetry associated with the traditional trip-to-trip connection-based approach for MDVSP, a new trip-to-route (T2R) connection-based approach is proposed. Considering real-world problem characteristics with numerous trips sharing common origin–destination stations and travel times on one route, this approach aggregates same vehicle possible trip sequences into a T2R connection. Two time-space network aggregation (TSNA) flow formulation versions, route pair-based TSNA and station pair-based TSNA, were constructed. Furthermore, TSNA equivalence under any given decomposition strategy, including first-in-first-out, with the multicommodity network flow (MCNF) model was demonstrated. Given the favorable separable TSNA structure, an alternating direction method of multipliers (ADMM)-based procedure is proposed to decompose the MDVSP into multiple subproblems that can be linearized and readily solved using commercial solvers. The quality of the solutions was assessed using lower bounds obtained from the Lagrangian relaxation problem. The effectiveness and superiority of the proposed MDVSP models and algorithms were subsequently confirmed using random data sets and real-world instances.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140651278","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}
Tianyong Jiang, Lingyun Li, Bijan Samali, Yang Yu, Ke Huang, Wanli Yan, Lei Wang
To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi-damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3-FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused-MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi-damage recognition proficiency for concrete bridges across varying field-of-view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real-time multi-damage detection for concrete bridges under complex environments.
为了解决传统混凝土桥梁多损伤识别方法固有的识别精度低、速度慢、泛化能力弱等难题，本文提出了一种高效的轻量级损伤识别模型，该模型是在 You only look once v4（YOLOv4）的基础上，利用 MobileNetv3 和融合反转残差块构建而成，命名为 YOLOMF。首先，构建了一个名为 MobileNetv3 和融合反转残差（MobileNetv3-FusedIR）的新型轻量级网络，作为 YOLOMF 的主干网络。这是通过将融合移动倒置瓶颈卷积（Fused-MPConv）集成到 MobileNetv3 的浅层来实现的。其次，YOLOv4 中的标准卷积被深度可分离卷积所取代，从而减少了模型的参数数量和复杂度。第三，深入研究了不同激活函数对 YOLOMF 损伤识别性能的影响。最后，为了验证所提出的方法在复杂环境中的有效性，我们使用了一个名为 Imgaug 的数据增强库来模拟混凝土桥梁在运动模糊、雾、雨、雪、噪声和颜色变化等挑战条件下的损坏图像。结果表明，YOLOMF 在不同视场尺寸和复杂环境条件下对混凝土桥梁的多损伤识别能力非常出色。YOLOMF 的检测速度达到 85f/s，有助于在复杂环境下对混凝土桥梁进行有效的多损伤实时检测。
{"title":"Lightweight object detection network for multi-damage recognition of concrete bridges in complex environments","authors":"Tianyong Jiang, Lingyun Li, Bijan Samali, Yang Yu, Ke Huang, Wanli Yan, Lei Wang","doi":"10.1111/mice.13219","DOIUrl":"https://doi.org/10.1111/mice.13219","url":null,"abstract":"To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi-damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3-FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused-MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi-damage recognition proficiency for concrete bridges across varying field-of-view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real-time multi-damage detection for concrete bridges under complex environments.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140651403","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}
Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time-consuming, limiting architectural creativity in the early-stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U-Net architecture, to rapidly predict wind in simplified urban environments, representative of early-stage design. The process of generating a wind field prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image-to-image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in less than 1 ms. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade-off between accuracy and computational efficiency. The fastest configuration is close to seven times faster, while having a relative loss, which is 1.8 times higher than the most accurate configuration. This CNN-based approach provides a fast and efficient method for pedestrian wind comfort (PWC) analysis, potentially aiding in more efficient urban design processes.
{"title":"Rapid pedestrian-level wind field prediction for early-stage design using Pareto-optimized convolutional neural networks","authors":"Alfredo Vicente Clemente, Knut Erik Teigen Giljarhus, Luca Oggiano, Massimiliano Ruocco","doi":"10.1111/mice.13221","DOIUrl":"https://doi.org/10.1111/mice.13221","url":null,"abstract":"Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time-consuming, limiting architectural creativity in the early-stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U-Net architecture, to rapidly predict wind in simplified urban environments, representative of early-stage design. The process of generating a wind field prediction at pedestrian level is reformulated from a 3D CFD simulation into a 2D image-to-image translation task, using the projected building heights as input. Testing on standard consumer hardware shows that our model can efficiently predict wind velocities in urban settings in less than 1 ms. Further tests on different configurations of the model, combined with a Pareto front analysis, helped identify the trade-off between accuracy and computational efficiency. The fastest configuration is close to seven times faster, while having a relative loss, which is 1.8 times higher than the most accurate configuration. This CNN-based approach provides a fast and efficient method for pedestrian wind comfort (PWC) analysis, potentially aiding in more efficient urban design processes.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140651510","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}
Tian‐yu Zhang, En‐jian Yao, Yang Yang, Hong‐Ming Yang, Dong‐bo Guo, David Z. W. Wang
This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits and rationalization of facility utilization is developed: (i) EV‐expanded network, (ii) multiagent‐based dynamic traffic assignment (MA‐DTA), and (iii) deployment refinement. Embedding the MA‐DTA and customized refinement strategy into the iterative planning structure enables the integration of operations and planning of the EFCS network. A numerical experiment and an empirical study in the Shandong Peninsula urban agglomeration are conducted. It demonstrates that the method can find a high‐quality solution within acceptable computation time and is applicable to realistic large‐scale EFCS planning. The planning method can effectively play the role of economy and facility in inducing EV users' charging demands, thus enhancing the overall ESER benefits. The integration of operation and planning is proven effective in reasonably matching the supply and demand of facility service and charging loads in a full‐time period. Further, the multistage EFCS planning schemes during 2025–2045 are explored, and some insightful policy implications are revealed.
{"title":"Multistage charging facility planning on the expressway coordinated with the power structure transformation","authors":"Tian‐yu Zhang, En‐jian Yao, Yang Yang, Hong‐Ming Yang, Dong‐bo Guo, David Z. W. Wang","doi":"10.1111/mice.13216","DOIUrl":"https://doi.org/10.1111/mice.13216","url":null,"abstract":"This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits and rationalization of facility utilization is developed: (i) EV‐expanded network, (ii) multiagent‐based dynamic traffic assignment (MA‐DTA), and (iii) deployment refinement. Embedding the MA‐DTA and customized refinement strategy into the iterative planning structure enables the integration of operations and planning of the EFCS network. A numerical experiment and an empirical study in the Shandong Peninsula urban agglomeration are conducted. It demonstrates that the method can find a high‐quality solution within acceptable computation time and is applicable to realistic large‐scale EFCS planning. The planning method can effectively play the role of economy and facility in inducing EV users' charging demands, thus enhancing the overall ESER benefits. The integration of operation and planning is proven effective in reasonably matching the supply and demand of facility service and charging loads in a full‐time period. Further, the multistage EFCS planning schemes during 2025–2045 are explored, and some insightful policy implications are revealed.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140648942","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}
Weixi Ren, Bo Yu, Yuren Chen, Kun Gao, Shan Bao, Zhixuan Wang, Yuting Qin
This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers’ visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self‐explaining theory. Then, Cycle‐consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety.
{"title":"An intelligent optimization method for the facility environment on rural roads","authors":"Weixi Ren, Bo Yu, Yuren Chen, Kun Gao, Shan Bao, Zhixuan Wang, Yuting Qin","doi":"10.1111/mice.13209","DOIUrl":"https://doi.org/10.1111/mice.13209","url":null,"abstract":"This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers’ visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self‐explaining theory. Then, Cycle‐consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140642554","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}
Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and hand‐held scanner (HHS). This approach considers both the global data and the local details of the steel members. Additionally, a precise registration method based on magnetic 3D targets is proposed for multiscale point clouds, which enables the registration accuracy of multisource point clouds to reach submillimeter precision. Subsequently, a novel algorithm for feature point screening is introduced, which utilizes a dichotomous point cloud grid approach to identify and extract a significant quantity of bolt holes effectively. This approach enables fully automated and fast extraction of the point cloud on the cylindrical inner surface of the holes. Finally, the bounding box and Procrustes analysis approach are employed to perform VTA using the point cloud of the cylindrical bolt holes as the assembled features. The accuracy and feasibility of the above method are verified by a down‐scale modeling experiment and project test, which provide technical support for the VTA of large steel truss structures.
{"title":"Virtual trial assembly of large steel members with bolted connections based on multiscale point cloud fusion","authors":"Zeyu Zhang, Dong Liang, Haibin Huang, Lu Sun","doi":"10.1111/mice.13210","DOIUrl":"https://doi.org/10.1111/mice.13210","url":null,"abstract":"Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and hand‐held scanner (HHS). This approach considers both the global data and the local details of the steel members. Additionally, a precise registration method based on magnetic 3D targets is proposed for multiscale point clouds, which enables the registration accuracy of multisource point clouds to reach submillimeter precision. Subsequently, a novel algorithm for feature point screening is introduced, which utilizes a dichotomous point cloud grid approach to identify and extract a significant quantity of bolt holes effectively. This approach enables fully automated and fast extraction of the point cloud on the cylindrical inner surface of the holes. Finally, the bounding box and Procrustes analysis approach are employed to perform VTA using the point cloud of the cylindrical bolt holes as the assembled features. The accuracy and feasibility of the above method are verified by a down‐scale modeling experiment and project test, which provide technical support for the VTA of large steel truss structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140642529","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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