scholarly journals Modeling and Simulation of a Robotic Bridge Inspection System

2020 ◽  
Vol 168 ◽  
pp. 177-185
Author(s):  
Muhammad Monjurul Karim ◽  
Cihan H. Dagli ◽  
Ruwen Qin
Keyword(s):  
Modeling And Simulation ◽  
Inspection System ◽  
Bridge Inspection

Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning

Measurement ◽  
2020 ◽  
Vol 164 ◽  
pp. 108048 ◽  
Author(s):  
Brandon J. Perry ◽  
Yanlin Guo ◽  
Rebecca Atadero ◽  
John W. van de Lindt
Keyword(s):  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Inspection System ◽  
Bridge Inspection ◽  
Aerial Vehicles

A lightweight bridge inspection system using a dual-cable suspension mechanism

2014 ◽  
Vol 46 ◽  
pp. 52-63 ◽  
Author(s):  
Yi-Chu Chen ◽  
Chi-En Yang ◽  
Shih-Chung Kang
Keyword(s):  
Inspection System ◽  
Bridge Inspection

scholarly journals ERROR SOURCES IN PROCCESSING LIDAR BASED BRIDGE INSPECTION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 455-459
Author(s):  
H. Bian ◽  
S. E. Chen ◽  
W. Liu
Keyword(s):  
Quality Management ◽  
Data Processing ◽  
Data Quality ◽  
Visual Inspection ◽  
Inspection System ◽  
Bridge Inspection ◽  
Error Sources ◽  
Scanning Angle ◽  
Management Framework ◽  
Inspection Data

Bridge inspection is a critical task in infrastructure management and is facing unprecedented challenges after a series of bridge failures. The prevailing visual inspection was insufficient in providing reliable and quantitative bridge information although a systematic quality management framework was built to ensure visual bridge inspection data quality to minimize errors during the inspection process. The LiDAR based remote sensing is recommended as an effective tool in overcoming some of the disadvantages of visual inspection. In order to evaluate the potential of applying this technology in bridge inspection, some of the error sources in LiDAR based bridge inspection are analysed. The scanning angle variance in field data collection and the different algorithm design in scanning data processing are the found factors that will introduce errors into inspection results. Besides studying the errors sources, advanced considerations should be placed on improving the inspection data quality, and statistical analysis might be employed to evaluate inspection operation process that contains a series of uncertain factors in the future. Overall, the development of a reliable bridge inspection system requires not only the improvement of data processing algorithms, but also systematic considerations to mitigate possible errors in the entire inspection workflow. If LiDAR or some other technology can be accepted as a supplement for visual inspection, the current quality management framework will be modified or redesigned, and this would be as urgent as the refine of inspection techniques.

scholarly journals Development of new bridge inspection system using 5G and AI under Cloud condition

2021 ◽  
Author(s):  
Mai Yoshikura ◽  
Takahiro Minami ◽  
Tomotaka Fukuoka ◽  
Makoto Fujiu ◽  
Junichi Takayama ◽  
...  
Keyword(s):  
Mobile Communication ◽  
High Speed ◽  
New Technology ◽  
Image Data ◽  
Growth Period ◽  
Concrete Bridges ◽  
Information And Communications Technology ◽  
Inspection System ◽  
Bridge Inspection ◽  
Inspection Work

In Japan, the deterioration of bridges constructed in the high economic growth period is progressing, and the maintenance of those bridges is a problem. Municipalities, as road administrators, are required to conduct close visual inspections of bridges (longer than 2m) once every five years, but municipalities face difficulties due to lack of financial and human resources. For this reason, the research of inspection and diagnosis technology is advanced for efficient inspection work. Especially, it is the new technology of ICT(information and communications technology), such as AI analysis of image data of bridge photographs. In this study, we developed a bridge inspection support system that automatically detects cracks in concrete bridges from bridge photographs. This system uses AI of image processing by deep learning. By using AI, we will be able to detect cracks in a short time and inspect bridges more efficiently. However, it requires many photographs of huge amount of data for image analysis. And those images take time to upload to the system by mobile communication. Therefore, we verified the system operation using 5G mobile communication, which is characterized by high speed and large capacity.

scholarly journals Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Haidong Sun ◽  
Zhengtao Zhang ◽  
Peng Li
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Wireless Communication ◽  
The Internet ◽  
Inspection System ◽  
Bridge Inspection ◽  
Inspection Systems ◽  
Inspection Data ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The continuous development of information technology and various electronic devices has accelerated the process of informatization and digitization, enabling the development and application of the emerging technology of wireless communication and the Internet of Things. Since the continuous occurrence of vicious bridge collapse accidents in China in recent years, the problem of bridge inspection has become a hot topic among the people. At the same time, how to apply wireless communication and the Internet of Things technology to bridge inspection systems has also become a new research topic. This article mainly studies the design and analysis of bridge detection systems based on wireless communication and Internet of Things technology. In order to expand the field of bridge detection and standard management and improve the credibility and reliability of safety problem prediction and evaluation, the bridge detection system will integrate IoT sensing, internet, remote communication, digital signal analysis and processing, big data knowledge mining, big data prediction and other technologies, design and analysis of the main structure of roads and bridges, and other multifaceted knowledge fields and build a professional intelligent digital network based on bridge inspection data collection, monitoring, analysis, evaluation, and early warning. From design to use and maintenance of the bridge, a digital neural network spanning time and space throughout the life cycle is constructed to construct a digital brain with bridge sensing points as neurons. This paper uses high-power infrared sensor equipment, satellite positioning systems, sensor equipment, and other technical equipment to achieve the purpose of data communication and exchange and realize intelligent positioning, identification, supervision, tracking, and other functions, making the wireless communication and Internet of Things reliable transmission, comprehensive perception, intelligent processing, and other capabilities very effective in the field of bridge inspection. Through the research and analysis of this article, there are more and more bridge inspection systems developed by the Internet of Things and wireless communication technology in China, and the percentage of related equipment used can reach more than 90%. The functions of the bridge inspection system are becoming more and more complete, and the results of the inspection data are also increasing.

DEVELOPMENT ON MAGNET MOVING ROBOT FOR BRIDGE INSPECTION SYSTEM

2018 ◽  
Vol 74 (2) ◽  
pp. I_1-I_9
Author(s):  
Hisato NAGATA ◽  
Eiji KOYANAGI ◽  
Yoshihiro NISHIOKA
Keyword(s):  
Inspection System ◽  
Bridge Inspection

scholarly journals Evaluation of Illinois Bridge Deterioration Models

2021 ◽  
Author(s):  
Gongkang Fu ◽  
Keyword(s):  
Probabilistic Models ◽  
Inspection System ◽  
Department Of Transportation ◽  
Deterministic Models ◽  
Bridge Inspection ◽  
Condition Rating ◽  
Bridge Maintenance ◽  
National Bridge Inventory ◽  
Transition Times

The National Bridge Inventory bridge inspection system ranks the condition of bridge components on a scale of zero to nine. The resulting condition ratings represent an important element considered in deciding measures for bridge maintenance, repair, and rehabilitation. Thus, forecasting future condition ratings well is critical to reliable planning for these activities and estimating the costs. The Illinois Department of Transportation currently has deterministic models for this purpose. This study’s objective is to review the current models using condition rating histories gathered from 1980 to 2020 in Illinois for the following bridge components: deck, superstructure, substructure, culvert, and deck beam. The results show the current Illinois Department of Transportation models are inadequate in forecasting condition ratings, producing overestimates of the transition times between two condition rating levels for these components / systems, except for the deck beam, which is underestimated. It is recommended that the mean transition times found in this study from condition rating histories are used to replace the current models as a short-term solution. Further research is recommended to develop probabilistic models as a long-term solution to address observed significant variation or uncertainty in condition rating and transition times between condition rating levels.

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