U‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic‐cuckoo optimization algorithm (HGCOA) with double‐point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U‐shaped ACTs
U 型自动化集装箱码头 (ACT) 是港口基础设施中的一项战略性设计，有利于同时进行装卸作业。本文探讨了在 U 型自动化集装箱码头调度多种类型设备（如双小车码头起重机 (DTQC)、自动导引车 (AGV)、双悬臂轨道起重机 (DCRC) 和外部卡车 (ET)）所面临的挑战。本文提出了优化多设备调度的混合整数线性规划模型，旨在同时最小化集装箱完成时间和 AGV 等待时间。本文定制了一种混合遗传-布谷鸟优化算法（HGCOA），采用双点交叉和莱维飞行布谷鸟搜索策略。大量数值结果表明，与精确求解方法相比，所提出的 HGCOA 在求解质量和计算时间方面均优于基准遗传算法，同时在不大幅牺牲求解质量的情况下显著提高了效率。总之，本研究为提高 U 型 ACT 的协调和运行效率提出了一种很有前途的解决方案。
{"title":"Optimizing multiple equipment scheduling for U‐shaped automated container terminals considering loading and unloading operations","authors":"Xiang Zhang, Ziyan Hong, Haoning Xi, Jingwen Li","doi":"10.1111/mice.13275","DOIUrl":"https://doi.org/10.1111/mice.13275","url":null,"abstract":"U‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic‐cuckoo optimization algorithm (HGCOA) with double‐point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U‐shaped ACTs","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177566","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}
Many cities find it difficult to claim enough land to build dedicated bicycle lanes. In response, this study proposes a novel framework to design a bicycle path network based on the existing sidewalks where selected sidewalk links are converted into eligible bicycle paths. The output will be a subset of the sidewalk links chosen to be converted to eligible bicycle paths with minimum cost such that all origin–destination (O‐D) pairs are connected with bicycle paths and cyclists from each O‐D pair can enjoy similar degrees of equity. The equity defined here is that cyclists from each O‐D pair will not need to travel excessively longer in time in the designed bicycle path network than in the original sidewalk network. A novel decomposition‐based dynamic dimensional search is proposed to solve the problem. The numerical experiments of a university campus and Clementi town in Singapore have shown our algorithm with varying equity parameter choices can provide tangible inclusive bicycle path network designs and improve as many as 80% equity in certain O‐D pairs with critical inequity issues.
{"title":"Sidewalk‐based bicycle path network design incorporating equity in cycling time","authors":"Yutong Cai, Ghim Ping Ong, Qiang Meng","doi":"10.1111/mice.13240","DOIUrl":"https://doi.org/10.1111/mice.13240","url":null,"abstract":"Many cities find it difficult to claim enough land to build dedicated bicycle lanes. In response, this study proposes a novel framework to design a bicycle path network based on the existing sidewalks where selected sidewalk links are converted into eligible bicycle paths. The output will be a subset of the sidewalk links chosen to be converted to eligible bicycle paths with minimum cost such that all origin–destination (O‐D) pairs are connected with bicycle paths and cyclists from each O‐D pair can enjoy similar degrees of equity. The equity defined here is that cyclists from each O‐D pair will not need to travel excessively longer in time in the designed bicycle path network than in the original sidewalk network. A novel decomposition‐based dynamic dimensional search is proposed to solve the problem. The numerical experiments of a university campus and Clementi town in Singapore have shown our algorithm with varying equity parameter choices can provide tangible inclusive bicycle path network designs and improve as many as 80% equity in certain O‐D pairs with critical inequity issues.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177577","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}
Lapone Techapinyawat, Aaliyah Timms, Jim Lee, Yuxia Huang, Hua Zhang
The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km2 (or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km2 (or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure.
{"title":"Integrated urban land cover analysis using deep learning and post‐classification correction","authors":"Lapone Techapinyawat, Aaliyah Timms, Jim Lee, Yuxia Huang, Hua Zhang","doi":"10.1111/mice.13277","DOIUrl":"https://doi.org/10.1111/mice.13277","url":null,"abstract":"The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km<jats:sup>2</jats:sup> (or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km<jats:sup>2</jats:sup> (or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177572","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}
Ehsan Kamjoo, Alireza Rostami, Fatemeh Fakhrmoosavi, Ali Zockaie
This study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)-dedicated lane implementation at large-scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi-level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV-dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human-driven vehicles to a fully AV environment.
本研究引入了一个框架，以最大限度地提高在大规模交通网络中实施自动驾驶汽车（AV）专用车道所带来的社会效益，同时考虑旅行时间的节省以及为其部署准备基础设施所需的投资。为此，提出了一个双层优化问题。上层决定专用车道部署的环节，下层则采用介观交通仿真工具，以真实再现混合交通中的这些车辆。该问题采用遗传算法解决。为了进一步减轻计算负担，本研究采用了一种基于蛇形算法的聚类方法，对候选链路进行分组并缩小解空间的大小。考虑到不同的需求水平、视听市场渗透率和实施方法，将所提出的框架成功应用于芝加哥市中心网络的案例研究。研究结果凸显了优化 AV 专用车道（AVDL）布局的必要性，以确保在不同场景下采用这一策略都能带来经济效益。这项研究为交通规划者提供了重要的运营见解，有助于在从人类驾驶车辆向完全的 AV 环境过渡的阶段有效采用 AVDL。
{"title":"A simulation-based approach for optimizing the placement of dedicated lanes for autonomous vehicles in large-scale networks","authors":"Ehsan Kamjoo, Alireza Rostami, Fatemeh Fakhrmoosavi, Ali Zockaie","doi":"10.1111/mice.13278","DOIUrl":"https://doi.org/10.1111/mice.13278","url":null,"abstract":"This study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)-dedicated lane implementation at large-scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi-level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV-dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human-driven vehicles to a fully AV environment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159732","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}
In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.
{"title":"Two-stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification","authors":"Wen-Jing Zhang, Ka-Veng Yuen, Wang-Ji Yan","doi":"10.1111/mice.13237","DOIUrl":"https://doi.org/10.1111/mice.13237","url":null,"abstract":"In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159762","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}
Mingyu Zhang, Lei Wang, Shuai Han, Shuyuan Wang, Heng Li
Autonomous equipment is playing an increasingly important role in construction tasks. It is essential to equip autonomous equipment with powerful 3D detection capability to avoid accidents and inefficiency. However, there is limited research within the construction field that has extended detection to 3D. To this end, this study develops a light detection and ranging (LiDAR)-based deep-learning model for the 3D detection of workers on construction sites. The proposed model adopts a voxel-based anchor-free 3D object detection paradigm. To enhance the feature extraction capability for tough detection tasks, a novel Transformer-based block is proposed, where the multi-head self-attention is applied in local grid regions. The detection model integrates the Transformer blocks with 3D sparse convolution to extract wide and local features while pruning redundant features in modified downsampling layers. To train and test the proposed model, a LiDAR point cloud dataset was created, which includes workers in construction sites with 3D box annotations. The experiment results indicate that the proposed model outperforms the baseline models with higher mean average precision and smaller regression errors. The method in the study is promising to provide worker detection with rich and accurate 3D information required by construction automation.
{"title":"Deep learning framework with Local Sparse Transformer for construction worker detection in 3D with LiDAR","authors":"Mingyu Zhang, Lei Wang, Shuai Han, Shuyuan Wang, Heng Li","doi":"10.1111/mice.13238","DOIUrl":"https://doi.org/10.1111/mice.13238","url":null,"abstract":"Autonomous equipment is playing an increasingly important role in construction tasks. It is essential to equip autonomous equipment with powerful 3D detection capability to avoid accidents and inefficiency. However, there is limited research within the construction field that has extended detection to 3D. To this end, this study develops a light detection and ranging (LiDAR)-based deep-learning model for the 3D detection of workers on construction sites. The proposed model adopts a voxel-based anchor-free 3D object detection paradigm. To enhance the feature extraction capability for tough detection tasks, a novel Transformer-based block is proposed, where the multi-head self-attention is applied in local grid regions. The detection model integrates the Transformer blocks with 3D sparse convolution to extract wide and local features while pruning redundant features in modified downsampling layers. To train and test the proposed model, a LiDAR point cloud dataset was created, which includes workers in construction sites with 3D box annotations. The experiment results indicate that the proposed model outperforms the baseline models with higher mean average precision and smaller regression errors. The method in the study is promising to provide worker detection with rich and accurate 3D information required by construction automation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159772","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}
Miguel E. Vázquez‐Méndez, Gerardo Casal, Alberte Castro, Duarte Santamarina
The constant passage of trains on the railways tracks causes, in the course of time, deviations that must be corrected periodically by means of a track calibration process. It consists of designing a new layout, called recreated horizontal alignment (RHA), as close as possible to the deformed center track fulfilling also the technical constraints according to the operational requirements of the railway. In recent years, different models have been proposed to address this task. This paper proposes, first, a new geometrical model that works with continuous variables for the definition of horizontal alignments (HAs) to deal with nonsymmetric transition curves at both sides of a circular curve and second, an optimization algorithm to compute the recreated alignment suitable in sinuous railway sections. This new mathematical model frees the optimization process from the need to previously identify the geometric elements (tangents, circular curves, and transition curves) of the HA. The usefulness of this model is tested with two academic examples showing its good behavior and in a real case study, where this algorithm is compared with the solution adopted by the engineers in a section of the railway line Ourense–Monforte in the NW of Spain.
火车在铁路轨道上的不断通过，会在一段时间内产生偏差，必须通过轨道校准过程定期加以纠正。它包括设计一个新的布局，称为重新创建的水平对齐（RHA），尽可能接近变形的中心轨道，同时满足铁路运营要求的技术限制。近年来，针对这一任务提出了不同的模型。本文首先提出了一种新的几何模型，该模型使用连续变量来定义水平走线（HAs），以处理圆形曲线两侧的非对称过渡曲线；其次，提出了一种优化算法，用于计算适合蜿蜒铁路路段的重建走线。这种新的数学模型使优化过程无需事先确定 HA 的几何元素（切线、圆曲线和过渡曲线）。该模型的实用性通过两个学术案例和一个实际案例进行了测试，前者显示了该模型的良好性能，后者则将该算法与工程师在西班牙西北部奥伦塞-蒙福尔特铁路线某路段采用的解决方案进行了比较。
{"title":"A geometric‐identification–free mathematical model for recreating nonsymmetric horizontal railway alignments","authors":"Miguel E. Vázquez‐Méndez, Gerardo Casal, Alberte Castro, Duarte Santamarina","doi":"10.1111/mice.13230","DOIUrl":"https://doi.org/10.1111/mice.13230","url":null,"abstract":"The constant passage of trains on the railways tracks causes, in the course of time, deviations that must be corrected periodically by means of a track calibration process. It consists of designing a new layout, called recreated horizontal alignment (RHA), as close as possible to the deformed center track fulfilling also the technical constraints according to the operational requirements of the railway. In recent years, different models have been proposed to address this task. This paper proposes, first, a new geometrical model that works with continuous variables for the definition of horizontal alignments (HAs) to deal with nonsymmetric transition curves at both sides of a circular curve and second, an optimization algorithm to compute the recreated alignment suitable in sinuous railway sections. This new mathematical model frees the optimization process from the need to previously identify the geometric elements (tangents, circular curves, and transition curves) of the HA. The usefulness of this model is tested with two academic examples showing its good behavior and in a real case study, where this algorithm is compared with the solution adopted by the engineers in a section of the railway line Ourense–Monforte in the NW of Spain.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141085539","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 Research Article Cumulative absolute velocity prediction for earthquake early warning with deep learning by Yanwei Wang et al., https://doi.org/10.1111/mice.13065.
{"title":"Cover Image, Volume 39, Issue 11","authors":"","doi":"10.1111/mice.13239","DOIUrl":"https://doi.org/10.1111/mice.13239","url":null,"abstract":"<p><b>The cover image</b> is based on the Research Article <i>Cumulative absolute velocity prediction for earthquake early warning with deep learning</i> by Yanwei Wang et al., https://doi.org/10.1111/mice.13065.\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":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073770","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}
Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field.
路面裂缝自动检测系统依靠高分辨率成像和机器学习增强的图像处理能力，极大地促进了基础设施管理的发展。然而，图像和运动模糊对裂缝检测和分析的准确性提出了巨大挑战。然而，有关减轻运动模糊的研究仍然很少。本研究介绍了一种擅长去模糊和分割的有效图像处理系统，该系统采用了以 UNet 为生成器的生成对抗网络（GAN）和以 Wasserstein GAN with Gradient Penalty (WGAN-gp) 为损失函数。这种方法在路面裂缝图像去模糊方面表现出色，并提高了分割精度。使用清晰图像和人工模糊图像对模型进行了训练，WGAN-gp 的有效性超过了其他损失函数。这项研究创新性地提出，除了峰值信噪比（PSNR）和结构相似性（SSIM）之外，还可以通过分割准确性来评估去模糊质量，从而揭示出 PSNR 和 SSIM 可能无法完全反映路面裂缝图像的去模糊效果。对各种生成器（包括 UNet、轻量级 UNet、TransUNet、DeblurGAN、DeblurGAN-v2 和 MIMO-UNet）进行了广泛评估，确定了 UNet 在模拟运动模糊方面的卓越性能。利用实际运动模糊图像进行的验证证实了所建议模型的有效性。这些研究结果表明，基于 GAN 的模型在克服路面裂缝检测系统中的运动模糊难题方面具有巨大潜力，标志着该领域的显著进步。
{"title":"A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks","authors":"Yu Zhang, Lin Zhang","doi":"10.1111/mice.13231","DOIUrl":"https://doi.org/10.1111/mice.13231","url":null,"abstract":"Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074138","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}
Pavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO) framework and a multi‐agent reinforcement learning algorithm. The established model was demonstrated on a highway pavement network in the real‐world, compared to a previously built two‐stage bottom‐up (TSBU) model. The results showed that, compared to TSBU, SNO produced a 3.0% reduction in total costs and an average pavement performance improvement of up to 17.5%. It prefers concentrated M&R schedules and tends to take more frequent preventive maintenance to reduce costly rehabilitation. The results of this research are anticipated to provide practitioners with quantitative estimates of the possible impact of ignoring segment interdependencies in M&R planning.
{"title":"A multi‐agent reinforcement learning model for maintenance optimization of interdependent highway pavement networks","authors":"L. Yao, Z. Leng, J. Jiang, F. Ni","doi":"10.1111/mice.13234","DOIUrl":"https://doi.org/10.1111/mice.13234","url":null,"abstract":"Pavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO) framework and a multi‐agent reinforcement learning algorithm. The established model was demonstrated on a highway pavement network in the real‐world, compared to a previously built two‐stage bottom‐up (TSBU) model. The results showed that, compared to TSBU, SNO produced a 3.0% reduction in total costs and an average pavement performance improvement of up to 17.5%. It prefers concentrated M&R schedules and tends to take more frequent preventive maintenance to reduce costly rehabilitation. The results of this research are anticipated to provide practitioners with quantitative estimates of the possible impact of ignoring segment interdependencies in M&R planning.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140953959","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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