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Convolutional Neural Networks for Real-Time and Wireless Damage Detection

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Dynamics of Civil Structures, Volume 2

Abstract

Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable computational power which makes the utilization of centralized techniques relatively infeasible for wireless sensor networks. In this paper, the authors present a novel Wireless Sensor Network (WSN) based on One Dimensional Convolutional Neural Networks (1D CNNs) for real-time and wireless structural health monitoring (SHM). In this method, each CNN is assigned to its local sensor data only and a corresponding 1D CNN is trained for each sensor unit without any synchronization or data transmission. This results in a decentralized system for structural damage detection under ambient environment. The performance of this method is tested and validated on a steel grid laboratory structure.

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Authors and Affiliations

  1. Department of Civil Engineering, Qatar University, Doha, Qatar

    Onur Avci & Osama Abdeljaber

  2. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Serkan Kiranyaz

  3. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA

    Daniel Inman

Authors
  1. Onur Avci
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  2. Osama Abdeljaber
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  3. Serkan Kiranyaz
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  4. Daniel Inman
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Corresponding author

Correspondence to Onur Avci .

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Editors and Affiliations

  1. Department of Civil & Environment Engineering, Lehigh University, Bethlehem, PA, USA

    Shamim Pakzad

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Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D. (2020). Convolutional Neural Networks for Real-Time and Wireless Damage Detection. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12115-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-12115-0_17

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