Health Monitoring and Evaluation of Long-Span Bridges Based on Sensing and Data Analysis: A Survey

Aimed at the health monitoring and evaluation of bridges based on sensing technology, the monitoring contents of different structural types of long-span bridges were defined. Then, the definition, classification, selection principle, and installation requirements of the sensors were summarized. The...

Full description

Bibliographic Details
Main Authors: Jianting Zhou, Xiaogang Li, Runchuan Xia, Jun Yang, Hong Zhang
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/17/3/603
Description
Summary:Aimed at the health monitoring and evaluation of bridges based on sensing technology, the monitoring contents of different structural types of long-span bridges were defined. Then, the definition, classification, selection principle, and installation requirements of the sensors were summarized. The concept was proposed that new adaptable long-life sensors could be developed by new theories and new effects. The principle and methods to select controlled sections and optimize the layout design of measuring points were illustrated. The functional requirements were elaborated on about the acquisition, transmission, processing, and management of sensing information. Some advanced concepts about the method of bridge safety evaluation were demonstrated and technology bottlenecks in the current safety evaluation were also put forward. Ultimately, combined with engineering practices, an application was carried out. The results showed that new, intelligent, and reliable sensor technology would be one of the main future development directions in the long-span bridge health monitoring and evaluation field. Also, it was imperative to optimize the design of the health monitoring system and realize its standardization. Moreover, it is a heavy responsibility to explore new thoughts and new concepts regarding practical bridge safety and evaluation technology.
ISSN:1424-8220