An intelligent leakage detection method for diaphragm wall joints based on fiber Bragg grating sensors and intelligent algorithms. (30th June 2022)

• Record Type: Journal Article
• Authors: Shao, Yong; Chen, Congxin; Lu, Zude; Zheng, Yun; Zhang, Yapeng
• Is Part Of: Measurement. Volume 197(2022)
• Publication Date: 2022-06-30
• Subjects: Fiber Bragg grating -- Diaphragm wall -- Leakage detection -- Machine learning -- Deep learning -- Leakage assessment; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111339
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An intelligent leakage judgment method for diaphragm wall joints based on FBG sensing signals was proposed. The proposed method is effective and efficient in identifying and locating leakage through diaphragm walls. The test accuracy of the PSO-SVM-based prediction model reaches 100%. Abstract: The leakage detection method based on fiber Bragg grating (FBG) sensing technology is an effective method to determine leakage points in diaphragm wall joints. However, the detection method involves cumbersome data processing which causes manual data processing errors and costs time. In this work, an intelligent leakage detection method for diaphragm wall joints based on FBG sensing signals was proposed. Firstly, a field study of the leakage detection method was carried out in a new urban expressway in Hohhot, Inner Mongolia, China. The field monitoring data were compared with test data, and characteristics of the FBG wavelength curve and mechanism of leakage detection were analyzed. In addition, four intelligent detection models including a convolutional neural network, support vector machine (SVM), decision tree, and random forest were established. Results showed that the proposed method is effective and efficient in identifying and locating leakage through diaphragm walls.