Computer-Aided Civil and Infrastructure Engineering最新文献

英文中文

Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position 基于智能手机的高耐用应变传感器，精度达到亚像素级，摄像头位置可调

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-19 DOI: 10.1111/mice.13383

Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao

Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full-stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub-pixel level, with pixel-based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub-pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub-pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS-Silica features easy data acquisition, high precision, and long service term, offering a promising method for long-term measurement of both surface and internal structures.

计算机视觉应变传感器通常要求相机位置固定，这就限制了以像素级分辨率对结构表面变形的测量。此外，传感器的使用期限远远短于结构的设计使用期限。本文介绍了对高耐用计算机视觉传感器--微图像应变传感（MISS）--二氧化硅的研究，该传感器利用连接到内窥镜的智能手机进行测量。该传感器的量程为 0.05 ε，可对结构进行从加载到失效的全阶段应变测量。该传感器在测量过程中无需固定摄像头，为嵌入式计算机视觉传感器奠定了理论基础。测量精度从像素级提高到子像素级，基于像素的测量误差约为 8 µε（标准偏差约为 7 µε），子像素计算误差约为 6 µε（标准偏差约为 5 µε）。亚像素计算的测量精度和稳定性提高了约 30%。MISS-Silica 具有数据采集简单、精度高、使用寿命长等特点，为表面和内部结构的长期测量提供了一种可行的方法。

{"title":"Smartphone-based high durable strain sensor with sub-pixel-level accuracy and adjustable camera position","authors":"Pengfei Wu, Bo Lu, Huan Li, Weijie Li, Xuefeng Zhao","doi":"10.1111/mice.13383","DOIUrl":"https://doi.org/10.1111/mice.13383","url":null,"abstract":"Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes a smartphone connected to an endoscope for measurement. It is designed with a range of 0.05 ε, enabling full-stage strain measurement from loading to failure of structures. The sensor does not require the camera to be fixed during measurements, laying the theoretical foundation for embedded computer vision sensors. Measurement accuracy is improved from pixel level to sub-pixel level, with pixel-based measurement errors around 8 µε (standard deviation approximately 7 µε) and sub-pixel calculation errors around 6 µε (standard deviation approximately 5 µε). Sub-pixel calculation has approximately 30% enhancement in measurement accuracy and stability. MISS-Silica features easy data acquisition, high precision, and long service term, offering a promising method for long-term measurement of both surface and internal structures.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"6 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673114","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}

引用次数: 0

Reinforcement learning-based approach for urban road project scheduling considering alternative closure types 基于强化学习的城市道路项目调度方法，考虑替代封闭类型

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-19 DOI: 10.1111/mice.13365

S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi

Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.

城市人口、出行和机动化的增长继续导致对扩大道路容量的城市项目的需求增加。遗憾的是，这些项目也造成了交通延误、废气排放、驾驶员沮丧以及其他道路使用者的不便。为了缓解这些弊端，道路机构通常面临两种施工区设计选择：完全封闭道路并重新规划交通路线，或者实施部分封闭。这两种选择都会对施工期间的道路通行能力、出行成本以及项目工期和成本产生重大影响。本研究提出了一种决策方法，以帮助在全封闭道路和部分封闭道路之间做出选择。所提出的决策方法是一个双层优化问题：在上层，道路机构寻求道路施工的最佳时间安排，以最大限度地减少车辆净排放量和道路施工成本。问题的下层捕捉了两类旅行者的路线选择行为：理性旅行者（最大限度地减少旅行时间）和路径忠诚旅行者（不改变施工前的路线）。该双层混合整数非线性模型采用基于强化学习的算法（多臂匪徒引导的粒子群优化 [PSO] 技术）求解。计算实验表明，与传统的 PSO 算法相比，所提出的算法在求解质量方面更具优势。数值结果表明，如果路径忠诚旅行者的比例增加，则该机构需要在道路项目建设上投入更多资金，以实施部分封闭，避免车辆排放量大幅增加。

{"title":"Reinforcement learning-based approach for urban road project scheduling considering alternative closure types","authors":"S. E. Seilabi, M. Saneii, M. Pourgholamali, M. Miralinaghi, S. Labi","doi":"10.1111/mice.13365","DOIUrl":"https://doi.org/10.1111/mice.13365","url":null,"abstract":"Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"46 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673123","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}

引用次数: 0

Cover Image, Volume 39, Issue 23 封面图片，第 39 卷第 23 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-17 DOI: 10.1111/mice.13380

The cover image is based on the Article A multi-phase mechanical model of biochar–cement composites at the mesoscale by Muduo Li et al., https://doi.org/10.1111/mice.13307.

封面图片来自 Muduo Li 等人撰写的《中尺度生物炭-水泥复合材料的多相力学模型》一文，https://doi.org/10.1111/mice.13307。

引用次数: 0

A multi-perspective fusion model for operating speed prediction on highways using knowledge-enhanced graph neural networks 使用知识增强图神经网络的高速公路运行速度预测多视角融合模型

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-17 DOI: 10.1111/mice.13382

Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao

This study proposes a multi-perspective fusion model for operating speed prediction based on knowledge-enhanced graph neural networks, named RoadGNN-S. By utilizing message passing and multi-head self-attention mechanisms, RoadGNN-S can effectively capture the coupling impacts of multi-perspective alignment elements (i.e., two-dimensional design, 2.5-dimensional driving, and three-dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and R-squared values of RoadGNN-S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN-S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data-driven models, knowledge-enhanced RoadGNN-S effectively avoids the fundamental errors, improving the R-squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi-perspective fusion and knowledge enhancement techniques.

本研究提出了一种基于知识增强图神经网络的运行速度预测多视角融合模型，命名为 RoadGNN-S。通过利用消息传递和多头自关注机制，RoadGNN-S 可以有效捕捉多视角排列元素（即二维设计、2.5 维驾驶和三维空间视角）的耦合影响。驾驶模拟数据结果表明，RoadGNN-S 的均方根误差、平均绝对误差、平均绝对百分比误差和 R 平方值均优于其他经典深度学习算法。然后，在 RoadGNN-S 中引入先验知识（即公路几何供给、驾驶员期望和车辆动态），并通过现场观测实验验证了模型的预测准确性和可移植性。与上述数据驱动模型相比，知识增强型 RoadGNN-S 有效避免了基本误差，在预测乘用车和卡车运行速度方面的 R 平方值分别提高了 7.9% 和 10.7%。该研究结果有助于利用多视角融合和知识增强技术进行智能公路几何设计。

{"title":"A multi-perspective fusion model for operating speed prediction on highways using knowledge-enhanced graph neural networks","authors":"Jianqiang Gao, Bo Yu, Yuren Chen, Kun Gao, Shan Bao","doi":"10.1111/mice.13382","DOIUrl":"https://doi.org/10.1111/mice.13382","url":null,"abstract":"This study proposes a multi-perspective fusion model for operating speed prediction based on knowledge-enhanced graph neural networks, named RoadGNN-S. By utilizing message passing and multi-head self-attention mechanisms, RoadGNN-S can effectively capture the coupling impacts of multi-perspective alignment elements (i.e., two-dimensional design, 2.5-dimensional driving, and three-dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and <i>R</i>-squared values of RoadGNN-S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN-S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data-driven models, knowledge-enhanced RoadGNN-S effectively avoids the fundamental errors, improving the R-squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi-perspective fusion and knowledge enhancement techniques.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"76 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665228","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}

引用次数: 0

Adaptive compensation using long short-term memory networks for improved control performance in real-time hybrid simulation 利用长短期记忆网络进行自适应补偿，提高实时混合模拟的控制性能

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-16 DOI: 10.1111/mice.13378

Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun Zhang

Real-time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost-effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short-term memory (LSTM) network, termed CATS-LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS-LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS-LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS-LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.

实时混合模拟（RTHS）将结构系统分为数值子结构和实验子结构，为分析结构系统，尤其是大型或复杂结构系统提供了一种经济有效的解决方案。然而，这些子结构之间的执行系统不可避免地会引入延迟，影响 RTHS 的稳定性和准确性。为解决这一问题，本研究提出了一种基于条件自适应时间序列（CATS）补偿器和长短期记忆（LSTM）网络的自适应补偿方法，称为 CATS-LSTM。LSTM 模型可预测致动器响应以进行参数估计并计算预测误差，从而提高控制性能并减少延迟。通过一系列模拟和实验，验证了 CATS-LSTM 方法的有效性和 LSTM 预测的准确性。结果表明，所提出的 CATS-LSTM 方法优于 CATS 和相位引导 (PL) 方法。与 CATS 方法相比，拟议方法的最大延迟、均方根误差和峰值误差分别减少了 3 毫秒、3.66% 和 4.78%，而与 PL 方法相比，则分别减少了 12 毫秒、8.4% 和 10.05%。此外，与 CATS 方法相比，CATS-LSTM 方法对初始参数估计的敏感度明显降低，从而增强了鲁棒性，减轻了初始参数估计不准确或变化的影响。

{"title":"Adaptive compensation using long short-term memory networks for improved control performance in real-time hybrid simulation","authors":"Zhenfeng Lai, Yanhui Liu, Zhipeng Zhai, Jiajun Zhang","doi":"10.1111/mice.13378","DOIUrl":"https://doi.org/10.1111/mice.13378","url":null,"abstract":"Real-time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost-effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short-term memory (LSTM) network, termed CATS-LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS-LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS-LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS-LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642664","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}

引用次数: 0

Cover Image, Volume 39, Issue 22 封面图片，第 39 卷第 22 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-11-04 DOI: 10.1111/mice.13368

The cover image is based on the Article A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks by Yu Zhang and Lin Zhang, https://doi.org/10.1111/mice.13231. Image Credit: Lin Zhang.

封面图像基于文章《路面裂缝自动检测中去除运动模糊的生成式对抗网络方法》，作者：Yu Zhang 和 Lin Zhang，https://doi.org/10.1111/mice.13231。图片来源：Lin Zhang。

引用次数: 0

Cover Image, Volume 39, Issue 21 封面图片，第 39 卷第 21 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-10-22 DOI: 10.1111/mice.13359

The cover image is based on the Article A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data by Fan Ding et al., https://doi.org/10.1111/mice.13229.

封面图像基于 Fan Ding 等人撰写的文章《基于移动性特征和蜂窝信令数据顺序关系的城际交通模式识别混合方法》，https://doi.org/10.1111/mice.13229。

引用次数: 0

Cover Image, Volume 39, Issue 21 封面图片，第 39 卷第 21 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-10-22 DOI: 10.1111/mice.13360

The cover image is based on the Article Automated quantification of crack length and width in asphalt pavements by Zhe Li et al., https://doi.org/10.1111/mice.13344.

封面图像基于李哲等人的文章《沥青路面裂缝长度和宽度的自动化量化》，https://doi.org/10.1111/mice.13344。

引用次数: 0

Cover Image, Volume 39, Issue 20 封面图片，第 39 卷第 20 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-10-01 DOI: 10.1111/mice.13351

The cover image is based on the Article Aggregation formulation for on-site multidepot vehicle scheduling scenario by Yi Gao et al., https://doi.org/10.1111/mice.13217.

封面图像基于高毅等人的文章《现场多网点车辆调度场景下的聚合公式》，https://doi.org/10.1111/mice.13217。

引用次数: 0

Automated quantification of crack length and width in asphalt pavements 自动量化沥青路面的裂缝长度和宽度

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-18 DOI: 10.1111/mice.13344

Zhe Li, Tuo Zhang, Yi Miao, Jiupeng Zhang, Mehran Eskandari Torbaghan, Yinzhang He, Jiasheng Dai

Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1-fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.

沥青路面裂缝长度和宽度的快速、准确和全自动估算对于实现积极的资产管理至关重要，但主要由于自动图像分割的有效性以及裂缝宽度和长度估算算法的准确性受到限制，这给估算工作带来了巨大挑战。为应对这一挑战，本文介绍了专为沥青路面裂缝长度估算设计的分支生长（BG）算法，以及为裂缝宽度估算量身定制的优化 OrthoBoundary 算法。利用四种广泛采用的沥青路面裂缝分割深度学习模型，产生了四组不同的图像分割结果。随后，对两种裂缝尺寸估算算法的有效性进行了综合评估。研究结果表明，将 BG 算法、优化的 OrthoBoundary 算法和全卷积网络与 HRNet 骨干进行整合后，裂缝长度估算的预测准确率达到 80.21%，平均宽度估算的预测准确率达到 84.32%。此外，在分辨率为 3024 × 3024 的情况下，图像处理速度可保持在 5 秒左右，平均宽度估计速度比未经优化的 OrthoBoundary 算法快达 9.1 倍。这些结果标志着自动裂缝量化方法的进步，对加强民用基础设施维护实践具有重要意义。

{"title":"Automated quantification of crack length and width in asphalt pavements","authors":"Zhe Li, Tuo Zhang, Yi Miao, Jiupeng Zhang, Mehran Eskandari Torbaghan, Yinzhang He, Jiasheng Dai","doi":"10.1111/mice.13344","DOIUrl":"10.1111/mice.13344","url":null,"abstract":"<p>Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1-fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 21","pages":"3317-3336"},"PeriodicalIF":8.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142237072","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}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Computer-Aided Civil and Infrastructure Engineering

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀