Void detection and void defect control methods for large-scale immersed steel shell–concrete tunnels. (April 2023)

• Record Type: Journal Article
• Authors: Li, Songhui; Zhang, Yan; Feng, Shaokong
• Is Part Of: Tunnelling and underground space technology. Volume 134(2023)
• Publication Date: 2023-04
• Subjects: Steel–concrete-steel sandwich composite structure -- Void defect -- Non-destructive testing -- Full scale test -- Impact imaging method; Tunneling -- Periodicals; Underground construction -- Periodicals; Tunnels -- Periodicals; Underground areas -- Periodicals; 624.193
• DOI: 10.1016/j.tust.2023.105006
• Languages: English
• ISSNs: 0886-7798

Abstract

Highlights: Void detection on large-scale steel shell-concrete SSIT using impact imaging method. Theoretically derive the relationship between the impulse response and void volume. Propose impact response strength index as quantitative criterion for void detection. Void detection accuracy was verified up to 87.5% through a full-size SSIT model. Suggest optimization tips by studying influence factors of void occurrence pattern. Abstract: In the manufacturing process of sandwich-structured immersed tunnel (SSIT), the self-compacting concrete cannot be vibrated. Therefore, the bonding surface between the steel plate and concrete is susceptible to void defects, and the overall safety of the tunnel will be adversely affected. In this study, we performed void detection on large-scale steel shell-concrete SSITs using the impact imaging method and proposed some void defect control methods. The relationship between impulse response and the void volume was derived theoretically. The correlation between impact response strength and void depth was studied with a full-size model of a steel shell-concrete SSIT. A quantitative void detection criterion was proposed based on the statistical analysis, based on which the void detection accuracy was shown to reach 87.5% through experimental verification. At last, the detection method was applied to the Shenzhen-Zhongshan Tunnel. The detection results of 22 sections comprising 33, 334 compartments were analyzed to determine the void occurrence patterns in self-compacting concrete during compartment pouring, identify areas susceptible to void, and select the influencing factors. Accordingly, the corresponding optimization measures were put forward.