A dynamic strain prediction method for malfunction of sensors in buildings subjected to seismic loads using CWT and CNN
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a method using convolutional neural networks (CNNs) to predict structural strain responses when sensors fail. The approach integrates continuous wavelet transform (CWT) data for enhanced seismic load analysis.
Area Of Science
- Civil Engineering
- Structural Engineering
- Data Science
Background
- Structural health monitoring (SHM) is crucial for assessing structural safety.
- Sensor malfunctions and data loss are significant challenges in SHM systems.
- Accurate strain response prediction is vital for maintaining structural integrity.
Purpose Of The Study
- To propose a novel method for predicting structural strain responses during sensor malfunctions.
- To leverage convolutional neural networks (CNNs) for accurate strain prediction.
- To enhance CNN predictions by incorporating continuous wavelet transform (CWT) data.
Main Methods
- A CNN model was developed to predict time-history strain data.
- Input data included strain measurements from adjacent structural members and CWT results.
- The target output was the strain response of a member with a malfunctioning sensor.
Main Results
- The proposed CNN method effectively predicted strain responses in structural members with sensor failures.
- Integrating CWT data improved the CNN's ability to capture dynamic structural behavior during seismic events.
- Both numerical simulations on steel frames and experimental tests on reinforced concrete frames validated the method's effectiveness.
Conclusions
- The developed CNN-based approach offers a reliable solution for predicting structural strain responses with sensor damage.
- The incorporation of CWT data is effective in enhancing the prediction of nonlinear structural responses.
- This method contributes to more robust and resilient structural health monitoring systems.
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