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Innovative Sensing by Using Deep Learning Framework

  • Nur Sila Gulgec
  • Martin Takáč
  • Shamim N. Pakzad
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Structures experience large vibrations and stress variations during their life cycles. This causes reduction in their load-carrying capacity which is the main design criteria for many structures. Therefore, it is important to accurately establish the performance of structures after construction that often needs full-field strain or stress measurements. Many traditional inspection methods collect strain measurements by using wired strain gauges. These strain gauges carry a high installation cost and have high power demand. In contrast, this paper introduces a new methodology to replace this high cost with utilizing inexpensive data coming from wireless sensor networks. The study proposes to collect acceleration responses coming from a structure and give them as an input to deep learning framework to estimate the stress or strain responses. The obtained stress or strain time series then can be used in many applications to better understand the conditions of the structures. In this paper, designed deep learning architecture consists of multi-layer neural networks and Long Short-Term Memory (LSTM). The network achieves to learn the relationship between input and output by exploiting the temporal dependencies of them. In the evaluation of the method, a three-story steel building is simulated by using various dynamic wind and earthquake loading scenarios. The acceleration time histories under these loading cases are utilized to predict the stress time series. The learned architecture is tested on acceleration time series that the structure has never experienced.

Keywords

Structural health monitoring Long short-term memory Recurrent neural networks Deep neural network 

Notes

Acknowledgements

Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program, and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA). Martin Takáč was supported by National Science Foundation grant CCF-1618717 and CMMI-1663256.

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Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  • Nur Sila Gulgec
    • 1
  • Martin Takáč
    • 2
  • Shamim N. Pakzad
    • 1
  1. 1.Department of Civil and Environmental EngineeringLehigh UniversityBethlehemUSA
  2. 2.Department of Industrial and Systems EngineeringLehigh UniversityBethlehemUSA

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