Smart Structures and Systems

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A cable tension identification technology using percussion sound

  • Wang, Guowei (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University) ;
  • Lu, Wensheng (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University) ;
  • Yuan, Cheng (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University) ;
  • Kong, Qingzhao (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University)
  • Received : 2021.04.22
  • Accepted : 2021.10.20
  • Published : 2022.03.25
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Abstract

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 in-situ structural safety assessment.

Keywords

Acknowledgement

This research is financially supported by National Key Research Program of China (Grant No. 2020YFC1512500), National Natural Science Foundation of China (Grant Nos. 51638012 and 51978507), Science and Technology Commission of Shanghai Municipality (Grant No. 19DZ1201200), and Innovation Foundation for Universities Collaboration of Shandong Province (Grant No. XTZ201903).

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