Multiclass Damage Identification in a Full-scale Bridge using Optimally-tuned One-dimensional Convolutional Neural Network

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Abstract
In this paper, a novel method is proposed based on windowed-one dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time-series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN methods performs with superior accuracy for severe damage cases and works well with different type of damage types.