Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

Abstract

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.