Structural damage detection based on convolutional neural networks and population of bridges

Shuai Teng, Xuedi Chen, Gongfa Chen, Li Cheng, David Bassir

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

The CNN-based detection methods have been widely used in the field of structural health monitoring (SHM), however, they can only be used for individual structures under certain conditions; for the structures in-service, damage detection will be affected by a variety of external factors (unknown/uncertain load and geometric dimension, etc.). Therefore, in order to improve the applicability of the CNNs, their compatibility and robustness need to be thoroughly investigated. In this paper, a large number of random models were produced to establish a population of bridge structures, the damage features of the population were extracted by the CNN; subsequently, the CNN was applied to damage detection of the new randomly-created models. The results show that the best detection results (99.4% accuracy) can be obtained by using the acceleration signals as the CNN input. This demonstrates that the proposed method will expand the detection ability of the CNN beyond an individual structure.

Original languageEnglish
Article number111747
JournalMeasurement: Journal of the International Measurement Confederation
Volume202
DOIs
Publication statusPublished - Oct 2022

Keywords

  • Bridge structure
  • Convolutional neural network
  • Population of bridges
  • Structural damage detection
  • Vibration signal

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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