Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Corrigendum to “Deep spatial-temporal embedding for vehicle trajectory validation and refinement”","authors":"","doi":"10.1111/mice.13303","DOIUrl":"10.1111/mice.13303","url":null,"abstract":"<p>Zhang, T. T., Jin, P. J., Piccoli, B., & Sartipi, M. (2024). Deep spatial-temporal embedding for vehicle trajectory validation and refinement. Computer-Aided Civil and Infrastructure Engineering, 39, 1597−1615. https://doi.org/10.1111/mice.13160</p><p>In the “Methodology” section, Equation (2) “<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 <mspace></mspace>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <msub>\u0000 <mi>e</mi>\u0000 <mi>i</mi>\u0000 </msub>\u0000 <mo>,</mo>\u0000 <msub>\u0000 <mi>e</mi>\u0000 <mi>j</mi>\u0000 </msub>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <mo>=</mo>\u0000 <mfrac>\u0000 <mn>1</mn>\u0000 <mn>2</mn>\u0000 </mfrac>\u0000 <mspace></mspace>\u0000 <mfenced>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>+</mo>\u0000 <mfrac>\u0000 <mrow>\u0000 <msubsup>\u0000 <mi>e</mi>\u0000 <mi>i</mi>\u0000 <mi>T</mi>\u0000 </msubsup>\u0000 <mo>∗</mo>\u0000 <msub>\u0000 <mi>e</mi>\u0000 <mi>j</mi>\u0000 </msub>\u0000 </mrow>\u0000 <mrow>\u0000 <msub>\u0000 <mi>e</mi>\u0000 <mi>i</mi>\u0000 </msub>\u0000 <mn>2</mn>\u0000 <msub>\u0000 <mi>e</mi>\u0000 <mi>j</mi>\u0000 </msub>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </mfrac>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation>$S ( {{{e}_i},{{e}_j}} ) = frac{1}{2} left( {1 + frac{{e_i^T{mathrm{*}}{{e}_j}}}{{{{e}_i}2{{{mathrm{e}}}_{mathrm{j}}}2}}} right)$</annotation>\u0000 </semantics></math>” was incorrect. The correct equation should have been written as “<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 <mspace></mspace>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 ","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 16","pages":"2553"},"PeriodicalIF":8.5,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561375","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}

{"title":"Optimizing green splits in high‐dimensional traffic signal control with trust region Bayesian optimization","authors":"Yunhai Gong, Shaopeng Zhong, Shengchuan Zhao, Feng Xiao, Wenwen Wang, Yu Jiang","doi":"10.1111/mice.13293","DOIUrl":"https://doi.org/10.1111/mice.13293","url":null,"abstract":"Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"45 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561384","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}

{"title":"Automatic generation of architecture drawings from point clouds","authors":"Fengyu Zhang,&nbsp;Qingzhao Kong,&nbsp;Cheng Yuan,&nbsp;Peizhen Li","doi":"10.1111/mice.13302","DOIUrl":"10.1111/mice.13302","url":null,"abstract":"<p>Traditional methods for producing architectural drawings require extensive manual labor. This paper proposes an automated method for generating a comprehensive set of three-view drawings, including the standardized labeling of doors and annotation of dimensions and areas. The output drawings are software-readable and editable, and the method is applicable to intricate structures with non-orthogonal or curved walls. To fully validate the accuracy of the proposed method, two distinct building scenarios were selected for experimentation.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3477-3488"},"PeriodicalIF":8.5,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556988","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}

{"title":"Ego-planning-guided multi-graph convolutional network for heterogeneous agent trajectory prediction","authors":"Zihao Sheng,&nbsp;Zilin Huang,&nbsp;Sikai Chen","doi":"10.1111/mice.13301","DOIUrl":"10.1111/mice.13301","url":null,"abstract":"<p>Accurate prediction of the future trajectories of traffic agents is a critical aspect of autonomous vehicle navigation. However, most existing approaches focus on predicting trajectories from a static roadside perspective, ignoring the influence of autonomous vehicles’ future plans on neighboring traffic agents. To address this challenge, this paper introduces EPG-MGCN, an ego-planning-guided multi-graph convolutional network. EPG-MGCN leverages graph convolutional networks and ego-planning guidance to predict the trajectories of heterogeneous traffic agents near the ego vehicle. The model captures interactions through multiple graph topologies from four distinct perspectives: distance, visibility, ego planning, and category. Additionally, it encodes the ego vehicle's planning information via the planning graph and a planning-guided prediction module. The model is evaluated on three challenging trajectory datasets: ApolloScape, nuScenes, and next generation simulation (NGSIM). Comparative evaluations against mainstream methods demonstrate its superior predictive capabilities and inference speed.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3357-3374"},"PeriodicalIF":8.5,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556989","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}

{"title":"Cover Image, Volume 39, Issue 14","authors":"","doi":"10.1111/mice.13296","DOIUrl":"10.1111/mice.13296","url":null,"abstract":"<p><b>The cover image</b> is based on the Research Article <i>Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques</i> by Junghee Park et al., https://doi.org/10.1111/mice.13126.\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 14","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141521610","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}

{"title":"Cover Image, Volume 39, Issue 14","authors":"","doi":"10.1111/mice.13297","DOIUrl":"10.1111/mice.13297","url":null,"abstract":"<p><b>The cover image</b> is based on the Research Article <i>Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap</i> by Donglian Gu et al., https://doi.org/10.1111/mice.13287.\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 14","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495985","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}

{"title":"Automated signal-based evaluation of dynamic cone resistance via machine learning for subsurface characterization","authors":"Samuel Olamide Aregbesola,&nbsp;Yong-Hoon Byun","doi":"10.1111/mice.13294","DOIUrl":"10.1111/mice.13294","url":null,"abstract":"<p>Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time-consuming, and error-prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 16","pages":"2541-2552"},"PeriodicalIF":8.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489516","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}

{"title":"Computing-efficient video analytics for nighttime traffic sensing","authors":"Igor Lashkov,&nbsp;Runze Yuan,&nbsp;Guohui Zhang","doi":"10.1111/mice.13295","DOIUrl":"10.1111/mice.13295","url":null,"abstract":"<p>The training workflow of neural networks can be quite complex, potentially time-consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video-based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision-based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low-illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 22","pages":"3392-3411"},"PeriodicalIF":8.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462862","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}

{"title":"A rendering‐based lightweight network for segmentation of high‐resolution crack images","authors":"Honghu Chu, Diran Yu, Weiwei Chen, Jun Ma, Lu Deng","doi":"10.1111/mice.13290","DOIUrl":"https://doi.org/10.1111/mice.13290","url":null,"abstract":"High‐resolution (HR) crack images provide detailed structural assessments crucial for maintenance planning. However, the discrete nature of feature extraction in mainstream deep learning algorithms and computational limitations hinder refined segmentation. This study introduces a rendering‐based lightweight crack segmentation network (RLCSN) designed to efficiently predict refined masks for HR crack images. The RLCSN combines a deep semantic feature extraction architecture—merging Transformer with a super‐resolution boundary‐guided branch—to reduce environmental noise and preserve crack edge details. It also incorporates customized point‐wise refined rendering for training and inference, focusing computational resources on critical areas, and an efficient sparse training method to ensure efficient inference on commercial mobile computing platforms. Each RLCSN's components are validated through ablation studies and field tests, demonstrating its capability to enable unmanned aerial vehicle‐based inspections to detect cracks as narrow as 0.15 mm from a distance of 3 m, thereby enhancing inspection safety and efficiency.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"62 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448289","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}

{"title":"Modeling of spatially embedded networks via regional spatial graph convolutional networks","authors":"Xudong Fan, Jürgen Hackl","doi":"10.1111/mice.13286","DOIUrl":"https://doi.org/10.1111/mice.13286","url":null,"abstract":"Efficient representation of complex infrastructure systems is crucial for system-level management tasks, such as edge prediction, component classification, and decision-making. However, the complex interactions between the infrastructure systems and their spatial environments increased the complexity of network representation learning. This study introduces a novel geometric-based multimodal deep learning model for spatially embedded network representation learning, namely the <i>regional spatial graph convolutional network</i> (RSGCN). The developed RSGCN model simultaneously learns from the node's multimodal spatial features. To evaluate the network representation performance, the introduced RSGCN model is used to embed different infrastructure networks into latent spaces and then reconstruct the networks. A synthetic network dataset, a California Highway Network, and a New Jersey Power Network were used as testbeds. The performance of the developed model is compared with two other state-of-the-art geometric deep learning models, GraphSAGE and Spatial Graph Convolutional Network. The results demonstrate the importance of considering regional information and the effectiveness of using novel graph convolutional neural networks for a more accurate representation of complex infrastructure systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430603","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}