Intelligent Transportation Infrastructure最新文献_第3页

Automatic Detection of Deteriorated Inverted-T Patching using 3D Laser Imaging System Based on a True Story Indiana 基于印第安纳真实故事的三维激光成像系统自动检测变质的倒t补片

Intelligent Transportation Infrastructure Pub Date : 2022-11-04 DOI: 10.1093/iti/liac011

Yang Liu, Guangwei Yang, Kelvin C. P. Wang, Guolong Wang, J. Li, T. Nantung

{"title":"Automatic Detection of Deteriorated Inverted-T Patching using 3D Laser Imaging System Based on a True Story Indiana","authors":"Yang Liu, Guangwei Yang, Kelvin C. P. Wang, Guolong Wang, J. Li, T. Nantung","doi":"10.1093/iti/liac011","DOIUrl":"https://doi.org/10.1093/iti/liac011","url":null,"abstract":"\u0000 Deteriorated Inverted-T patching can lead to uneven settlement, dip, or reflective transverse cracking on the asphalt overlay. This paper demonstrates a hybrid method to automatically detect deteriorated Inverted-T patching for an efficient maintenance schedule. First, hundreds of 2D/3D pavement images with deteriorated Inverted-T patching were manually identified and labelled from more than 400 miles of field data in Indiana. All data were collected through a high-speed 3D laser imaging system. Afterward, three deep learning architectures, including the Single Shot Detector network (SSD300), an advanced Region-based Convolutional Neural Network (Mask R-CNN), and a fast and precise convolutional network (U-Net), were applied to develop artificial intelligence models to identify deteriorated Inverted-T patching from 3D images. The results indicate that the Mask R-CNN model can achieve good detection accuracy only on the prepared testing images. Further, a hybrid deep learning model was developed to combine International Roughness Index (IRI) values and the corresponding 3D images to detect deteriorated Inverted-T patching. The hybrid method was promising and significantly improved the efficiency of locating deteriorated Inverted-T patching from network screening.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126698838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly Supervised Convolutional Neural Network for Pavement Crack Segmentation 弱监督卷积神经网络用于路面裂缝分割

Intelligent Transportation Infrastructure Pub Date : 2022-11-04 DOI: 10.1093/iti/liac013

Youzhi Tang, Yu Qian, Enhui Yang

{"title":"Weakly Supervised Convolutional Neural Network for Pavement Crack Segmentation","authors":"Youzhi Tang, Yu Qian, Enhui Yang","doi":"10.1093/iti/liac013","DOIUrl":"https://doi.org/10.1093/iti/liac013","url":null,"abstract":"\u0000 Crack assessment plays an important role in pavement evaluation and maintenance planning. Recent studies leverage the powerful learning capability of Artificial Neural Networks (ANNs) and have achieved good performance with computer vision-based crack detectors. Most existing models are based on the Fully Supervised Learning (FSL) approach and heavily rely on the annotation quality to achieve reasonable accuracy. The annotation cost under the FSL approach has become nontrivial and often causes heavy burdens on model development and improvement, especially for complex networks with deep layers and a large number of parameters. To combat the image annotation cost, we proposed a novel Weakly Supervised Learning U-Net (WSL U-Net) for pavement crack segmentation. With the Weakly Supervised Learning (WSL) approach, the training of the network uses weakly labeled images instead of precisely labeled images. The weakly labeled images only need rough labeling, which can significantly alleviate the labor cost and human involvement in image annotation. The experimental results from this study indicate the proposed WSL U-Net outperforms some other Semi-Supervised Learning (Semi-SL) and WSL methods and achieves comparable performance with its FSL version. The dataset cross-validation shows that WSL U-Net outperforms FSL U-Net, suggesting the proposed WSL U-Net is more robust with fewer overfitting concerns and better generalization capability.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130775280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Manufacture, development, and application of sensor-enabled Geosynthetics: state-of-the-art review 传感器土工合成材料的制造、开发和应用:最新技术综述

Intelligent Transportation Infrastructure Pub Date : 2022-11-02 DOI: 10.1093/iti/liac012

Yi-lin Wang, Xin-zhuang Cui, Kaiwen Liu, P. Jiang

{"title":"Manufacture, development, and application of sensor-enabled Geosynthetics: state-of-the-art review","authors":"Yi-lin Wang, Xin-zhuang Cui, Kaiwen Liu, P. Jiang","doi":"10.1093/iti/liac012","DOIUrl":"https://doi.org/10.1093/iti/liac012","url":null,"abstract":"\u0000 The long-term in-situ monitoring of transportation infrastructure is a key necessity for intelligent traffic management, which requires the monitoring methods to have good performances on the distributed measurements, durability, robustness, and convenience. To offer an alternative for intelligent monitoring of transportation infrastructures, this paper introduces the development and application of an innovative material named sensor-enabled geosynthetics (SEG) derived from the tensoresistivity of conductive polymers. Unlike other monitoring mediums, the unique feature of the SEG is its two-fold function: in-situ reinforcement and monitoring. The manufacture process of SEG is introduced and the basic properties of SEG are investigated by laboratory tests. The corresponding constitutive models are established and employed in the theoretical analysis of SEG interacted with soil. Based on the experimental and theoretical approaches, a positioning, precursor identification and early warning method for the internal failure of subgrade is proposed and incorporated into the safety monitoring and early warning system for geotechnical engineering involving SEG. According to the application cases of SEG and the system in highway engineering, SEG is proved to perform excellently in terms of the durability, distributed measurements, wide measuring range, and negligible installation effect. Thus, it is considered as an innovative and reliable alternative for long-term in-situ monitoring of transportation infrastructures particularly in subgrade engineering.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on the research progress of mechanical meta-structures and their applications in rail transit 力学元结构及其在轨道交通中的应用研究进展

Intelligent Transportation Infrastructure Pub Date : 2022-10-29 DOI: 10.1093/iti/liac010

Linli Zhang, X. Sheng

引用次数: 2

Quantitative evaluation of the rotational stiffness of rail cracks based on the reflection of guided waves 基于导波反射的钢轨裂纹旋转刚度定量评价

Intelligent Transportation Infrastructure Pub Date : 2022-10-12 DOI: 10.1093/iti/liac008

Pingxin Liu, Shitao Liu, Chen-Li Yu, Limin Gu, Yu Zhou, Chunyu Zhao, Zhenyu Huang

引用次数: 0

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review 使用创新传感技术和机器学习算法的铁路桥梁结构健康监测:简要回顾

Intelligent Transportation Infrastructure Pub Date : 2022-09-30 DOI: 10.1093/iti/liac009

You-Wu Wang, Y. Ni, Sumei Wang

{"title":"Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review","authors":"You-Wu Wang, Y. Ni, Sumei Wang","doi":"10.1093/iti/liac009","DOIUrl":"https://doi.org/10.1093/iti/liac009","url":null,"abstract":"\u0000 Railway bridges are the vital element of the railway infrastructures, whose safety directly affects the regional economy and commuter transportation. However, railway bridges are often subjected severe loading and working conditions, caused by traffic growth and heavier vehicles, and the increase in train running speed further makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the safety and reliability of the overall railway bridges is the bridge structural health monitoring (SHM), which not only monitors the structural conditions of bridges and maintains train operation safety, but also helps to expand the lifespan of bridges by enhancing the durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provided a review or overview on SHM of railway bridges. Some of the review papers have become obsolete and are not reflective of the state-of-the-art research. Therefore, the main goal of this article is to summarize the state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. the fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Then the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, the issues related to the future directions and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Intelligent construction for the transportation infrastructure: a review 交通基础设施智能化建设研究综述

Intelligent Transportation Infrastructure Pub Date : 2022-09-22 DOI: 10.1093/iti/liac007

Qing He, F. Ghofrani, Tianci Gao, Ping Wang, Chuan He, Yongle Li, Changfa Ai

引用次数: 0

Efficient Approach to Automated Pavement Manhole Detection with Modified Faster R-CNN 基于改进更快R-CNN的路面人孔自动检测方法

Intelligent Transportation Infrastructure Pub Date : 2022-09-06 DOI: 10.1093/iti/liac006

Han Zhang, Zishuo Dong, Anzheng He, Allen A. Zhang, Kelvin C. P. Wang, Yang Liu, Jie Xu, Jing Shang, Changfa Ai

{"title":"Efficient Approach to Automated Pavement Manhole Detection with Modified Faster R-CNN","authors":"Han Zhang, Zishuo Dong, Anzheng He, Allen A. Zhang, Kelvin C. P. Wang, Yang Liu, Jie Xu, Jing Shang, Changfa Ai","doi":"10.1093/iti/liac006","DOIUrl":"https://doi.org/10.1093/iti/liac006","url":null,"abstract":"\u0000 Presently, more and more attention has been paid to the detection of road facilities. Pavement manhole is an important type of road facilities, and can result in tangible impacts on driving safety and comfort. This paper proposes a robust method based on a modification of the Faster Region Convolutional Neural Network (Faster R-CNN) to detect pavement manholes automatically. We establish a manually-annotated image library that consists of 1245 manhole images collected by 1-mm laser imaging system, and implement the modified Faster R-CNN architecture to locate manholes exclusively under realistic and complex environments. Compared with the original Faster R-CNN, the proposed modification is to replace the feature extractor used in the original Faster R-CNN with a more-efficient backbone ResNet50, and implement Feature Pyramid Network (FPN) to fuse multi-scale features. The experimental results demonstrate that the modified Faster R-CNN outperforms the original Faster R-CNN and other state-of-the-art models, including YOLOv4, EfficientDet and YOLOX. The F1-score and Overall-IOU achieved by the modified Faster R-CNN on 250 testing images are 98.15% and 92.07% respectively. To further verify the robustness of the proposed method, the modified Faster R-CNN is applied to process manhole images which are taken randomly by a smart phone and thus highly differ from manhole images acquired by the laser imaging system. It is found that the modified Faster R-CNN can also yield similar detection efficiency even for images representing highly dissimilar viewing angles and unforeseen scenarios, implying the benefits of deep-learning-based object detection algorithms to intelligent investigation of pavement manholes.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Quantifying Thermal Strain of Steel Plate Subjected to Constant Temperature by Distributed Fiber Optic Sensors 基于分布式光纤传感器的钢板恒温热应变定量分析

Intelligent Transportation Infrastructure Pub Date : 2022-07-25 DOI: 10.1093/iti/liac005

Yanping Zhu, Genda Chen

{"title":"Quantifying Thermal Strain of Steel Plate Subjected to Constant Temperature by Distributed Fiber Optic Sensors","authors":"Yanping Zhu, Genda Chen","doi":"10.1093/iti/liac005","DOIUrl":"https://doi.org/10.1093/iti/liac005","url":null,"abstract":"\u0000 Effective strain measurement tools for steel structure at high temperature are limited due to a significant gap in measurement science. This study aims to experimentally and numerically investigate effectiveness and limitation of Rayleigh scattering based, distributed fiber optic sensors (DFOS) without coatings for measuring temperature and strain of a steel plate subjected to a local constant temperature. The DFOS were bonded to the steel plate by an epoxy with different bond lengths to measure coupled strain and temperature effect, while the DFOS near the end of the epoxied segment measured the temperature effect only for temperature discrimination. It was found that the DFOS accurately measured the temperature and strain of the steel plate with different bond lengths of the epoxy, as compared to the thermocouple temperature and thermal-induced strain, respectively. The maximum strain (or temperature) that the DFOS without coatings could measure for the steel plate was less than 1600 $mu varepsilon$ (or 150${}^{{}^{circ}}C$). Moreover, a local finite element model with the calibrated elastic modulus of the epoxy subjected to a uniform temperature field well captured optical fiber strains in the elastic stage. From parametric studies, the effect of the thermal expansion coefficients and elastic moduli of the optical fiber, epoxy, and host material as well as initial defect between the optical fiber and epoxy on the strain transfer coefficient was investigated. The elastic modulus of epoxy within 100 MPa and the rectangular cross-section of epoxy (0.5 mm thick and 4 mm wide) could achieve a strain transfer coefficient of 0.997, while the initial defect had a similar effect on the strain transfer to the protective coating. The normal-distribution epoxy shape was designed for guiding robot assisted intelligent instrumentation and construction in the future.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A review of research on design theory and engineering practice of high-speed railway turnout 高速铁路道岔设计理论与工程实践研究综述

Intelligent Transportation Infrastructure Pub Date : 2022-06-28 DOI: 10.1093/iti/liac004

Ping Wang, Jing-mang Xu, Shuguo Wang, Yao Qian, Rong Chen, Fei Yang, Jiasheng Fang

{"title":"A review of research on design theory and engineering practice of high-speed railway turnout","authors":"Ping Wang, Jing-mang Xu, Shuguo Wang, Yao Qian, Rong Chen, Fei Yang, Jiasheng Fang","doi":"10.1093/iti/liac004","DOIUrl":"https://doi.org/10.1093/iti/liac004","url":null,"abstract":"\u0000 This paper systematically reviews the research progress, problems, specific countermeasures and development trends of the dynamic design theory as applied to high-speed railway turnouts. This includes wheel-rail contact solving, high-speed vehicle -turnout dynamic interaction simulation, analysis of long-term turnout performance deterioration, safety assessment of train passing through the turnout, and the maintenance and management of turnout serviceability. High-speed turnouts still face severe technical challenges with regard to their acclimation to the future development of rail transit technology. Some of these challenges include the suitability of next-generation higher-speed turnouts to a complex environment, life-cycle design, optimization of wheel-rail matching and vehicle-turnout dynamic performance, real-time status capture and performance assessment, health management and damage prediction. It is now necessary to deepen the basic theoretical study of high-speed railway turnouts, and integrate cutting-edge techniques, such as advanced materials and manufacturing, intelligence and automation, big data and cloud computing, in an effort to enhance China’s capabilities for original innovation in high-speed railway turnout technology. By analysing the present situation in a problem-orientated manner, this paper aims to provide a new perspective, as well as some basic data for academic research into technological innovations for railway engineering.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122731848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0