Damage detection of tunnel based on the high-density cross-sectional curvature obtained using strain data from BOTDA sensors. (September 2021)

• Record Type: Journal Article
• Authors: Liu, Yang; Li, Hu; Wang, Yongliang; Men, Yanqing; Xu, Qianen
• Is Part Of: Mechanical systems and signal processing. Volume 158(2021)
• Publication Date: 2021-09
• Subjects: Tunnel -- Damage detection of structures -- Brillouin optical time-domain analysis -- Cross-sectional curvature -- BP artificial neural network; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107728
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Discussing the condition required for converting the strain into cross-sectional curvature. Proposing a discriminative method of converting the strains into cross-sectional curvature. Detecting the damage of tunnel using strain data from BOTDA sensors. Abstract: Fully distributed Brillouin-scattering-based optical sensors—e.g., Brillouin optical time-domain analysis (BOTDA) sensors—appear to be promising supplementary tools for the SHM of actual tunnels. As far as we know, there is little research on detecting the damage of tunnels by using the strain continuous monitoring data obtained from BOTDA sensors. To address this issue, a method based on high-density cross-sectional curvature is proposed for the damage detection of operating tunnel. First, considering the long-distance and large-scale characteristics of actual tunnels, the strain is not sufficiently sensitive to structural damage; thus, the structural strain is converted into the cross-sectional curvature of the tunnel in order to improve this sensitivity. The condition required for converting the strain at the measured points into corresponding measurements of the cross-sectional curvature of the tunnel is discussed, and on this basis, a discriminative approach to the abovementioned conversion is presented. Second, a damage detection index based on high-density cross-sectional curvature is established, and combined with the novel detection approach, the proposed index is implemented to detect the damage of the tunnel. Finally, numerical examples are presented to analyze and discuss the noise resistance performance and structural damage sensitivity of the proposed method. Moreover, the effectiveness of the proposed method is demonstrated using strain monitoring data obtained from an actual tunnel with a box-type structure.