This repository is the official implementation of "Filter Banks and Hybrid Deep Learning Architectures for Performance-based Seismic Assessments of Bridges" by Seyed Omid Sajedi and Xiao Liang. Hybrid Deep learning models for Rapid Assessments (HyDRA) is introduced as a multi-branch neural network architecture that enable end-to-end training for different types of processed vibration data structures for structural damage diagnosis.
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The code is tested on a Python environment and CUDA installation compatible with tensorflow 2.6 (See here). After cloning this repository, make sure to download the Dataset from the latest Release. To optimize training and infererence, the dataset contains processed input features and labels from the seismic simulations. A subset bin of raw signals can also be downloaded from here to provide insight on feature extraction. After downloading the files, extract all the files together and make sure to updates the paths in the desired scripts.
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Two example notebooks are provided for the signal preprocessing and deep learning stages of this implementation.
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The code used to generate the benchmark results in the paper can be found inside the utils directory.
Please cite the following paper if you have used this repositpry.
- Parts of the code regarding the extraction of filter banks from the raw signals is based on Haytham Fayek's excellet tutorial on Speech Processing for Machine Learning.
- Mel Frequency Cepstral Coefficient (MFCC) tutorial by James Lyons is a great resource to obtain deep insight on the theoretical background of filter banks for speech processing.