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논문 기본 정보

자료유형
학술저널
저자정보
Qingzhao Kong (Tongji University) Guowei Wang (Tongji University) Wensheng Lu (Tongji University) Cheng Yuan (Tongji University)
저널정보
국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.29 No.3
발행연도
2022.3
수록면
475 - 484 (10page)

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The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for <i>in-situ</i> structural safety assessment.

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