Damage cross detection between bridges monitored within one cluster using the difference ratio of projected strain monitoring data. (March 2022)

• Record Type: Journal Article
• Authors: Cao, Jianxin; Liu, Yang; Li, Changping
• Is Part Of: Structural health monitoring. Volume 21:Number 2(2022)
• Page Start: 571
• Page End: 595
• Publication Date: 2022-03
• Subjects: Bridges monitored within one cluster -- damage cross-detection -- difference ratio of projected strain data -- degree of structural similarity -- time-varying environmental temperature; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217211006792
• Languages: English
• ISSNs: 1475-9217

Abstract

The bridges monitored within one cluster refer to several medium- and small-span beam bridges with the same or similar structural characteristics located in a local road network, and these bridges suffer the similar traffic and environmental loads. It is still little research on how to detect the damage of all bridges monitored within one cluster utilizing the above-mentioned characteristics of these bridges. To address this issue, a method is proposed for the damage cross-detection between bridges monitored within one cluster by using the difference ratio of projected strain monitoring data under time-varying environmental temperatures. First, a damage feature is established by using the difference ratio of projected strain monitoring data obtained from the same cross-section position of any two bridges monitored within one cluster that have similar or identical structural characteristics. On this basis, the relationship between the statistical characteristics of the proposed damage feature and the degree of structural similarity between two bridges are discussed in detail. Second, a damage detection index is presented by calculating the subspace angle between two damage features. Then, combined with kernel density estimation and a cross-validation strategy, the proposed index is implemented to detect the damage of all bridges monitored within one cluster. Finally, numerical simulation examples are utilized to analyse and discuss the application limitations, noise resistance performance and structural damage sensitivity of the proposed method. Moreover, the effectiveness of the proposed method is also demonstrated by using the strain monitoring data obtained from actual bridges monitored within one cluster.