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Structural Monitoring and Maintenance Volume 8, Number 4, December 2021 , pages 379-402 DOI: https://doi.org/10.12989/smm.2021.8.4.379 |
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Structural damage detection in presence of temperature variability using 2D CNN integrated with EMD |
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Smriti Sharma and Subhamoy Sen
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| Abstract | ||
| Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be computeintensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frameinvariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise. | ||
| Key Words | ||
| Convolutional Neural Network (CNN); damage detection; Deep Learning (DL); Empirical Mode Decomposition (EMD); Structural Health Monitoring (SHM) | ||
| Address | ||
| i4S Laboratory, Indian Institute of Technology Mandi, Mandi, HP, India. | ||
