One-dimensional convolutional neural network-based damage detection in structural joints

Abstract

Structural health monitoring research traditionally focuses on detecting damage in members excluding the possibility of weakened joint conditions. Efficient model-based joint damage detection algorithms demand computationally expensive model that may affect the promptness of detection. Deep learning techniques have recently come up as efficient alternative to this cause. These techniques help in predicting occurrence and location of damage in structures based on some automatically identified features embedded in the measured structural response. This article proposes an output-only approach for joint damage detection in which a 1D-convolutional neural network (CNN) has been introduced to locate weakened joints in semi-rigid frames. CNN architecture merges feature extraction and classification simultaneously within a single learning block to automatically extract abstract features from typically 2D/3D signals. Proposed approach further modifies the usual CNN architecture to enable it to handle 1D response signals. Numerical validation is performed on a 2D-steel frame under different damage locations and severities followed by experimental validation on a steel frame structure. The method is observed to be very precise and prompt in detecting single as well as multiple damage scenarios. False alarm sensitivity of the proposed algorithm is also tested and found to be well within acceptable limits.

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Correspondence to Subhamoy Sen.

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The authors declare that they have no conflict of interest.

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This study was funded by Aeronautics Research & Development Board (DRDO), New Delhi, India through grant file no. ARDB/01/1051907/M/I.

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Sharma, S., Sen, S. One-dimensional convolutional neural network-based damage detection in structural joints. J Civil Struct Health Monit 10, 1057–1072 (2020). https://doi.org/10.1007/s13349-020-00434-z

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Keywords

  • Structural health monitoring (SHM)
  • Joint damage detection
  • Machine learning (ML)
  • CNN