Development of bridge influence line identification methods based on direct measurement data: A comprehensive review and comparison. (1st November 2019)

• Record Type: Journal Article
• Authors: Zheng, Xu; Yang, Dong-Hui; Yi, Ting-Hua; Li, Hong-Nan
• Is Part Of: Engineering structures. Volume 198(2019)
• Publication Date: 2019-11-01
• Subjects: Bridge influence line -- Inverse problem -- B-WIM -- Bridge evaluation -- Damage detection -- Model correction; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.109539
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A comprehensive review of current research in bridge influence line identification methods is provided. Four different indexes of different methods are compared and the features of these methods are summarized. A criterion for selecting suitable influence line identification method in different conditions is provided. An outlook for further development of bridge influence line identification method is introduced. Abstract: Bridge influence line, which is defined as the response curve of a certain point of the bridge under the moving unit concentrated load, contains tremendous structural information. However, the real bridge influence lines seldom correspond well with them calculated by bridge model. Accordingly, exact identification of the bridge influence line from the direct measurement data grows up to be an important issue for bridge weight-in-motion system, performance evaluation, model correction and bridge damage detection. This paper provides a comprehensive review of current research and development activities in bridge influence line identification method. At first, the development and applications of the bridge influence line are introduced. Following that, the mathematical models of bridge influence line identification and different bridge influence line identification methods are provided. Finally, four different indexes including noise immunity, peak reconstruction accuracy, computational complexity, and adaptability for axle weight change of different methods are compared and the features of these methods are summarized. The end of this paper provides a criterion for selecting suitable influence line identification method in different conditions and an outlook for further development of bridge influence line identification method.