One-dimensional convolutional neural network for damage detection of structures using time series data

Structural health monitoring (SHM) has been a continuous interest in civil engineering for decades because of its importance to the smooth operation of structures. Machine learning (ML) and deep learning (DL) methods have been successfully applied for damage detection of structures. However, the traditional ML algorithms heavily depend on the features’ choice and the classifier; therefore, their application and accuracy are often limited for complex structural data. This study aims to develop a novel DL model, namely the one-dimensional convolutional neural network (1D-CNN), for the damage detection of structures using time series data. The 1D-CNN model can automatically extract the features of one-dimensional time series data without manual feature extraction. The proposed 1D-CNN model’s performance is evaluated using two experimental benchmark datasets of large-scale steel frames and numerical datasets of a suspension bridge subjected to seismic excitation via the confusion matrix, accuracy, sensitivity (recall), specificity, precision, F1-score, and area under curve (AUC) under receiver operating characteristic (ROC) curve. The results show that the proposed 1D-CNN model achieves superior accuracy for damage severity and damage localization identifications of structures. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.