Efficiency validation of one dimensional convolutional neural networks for structural damage detection using a SHM benchmark data

Abstract

In this paper, a novel one dimensional convolution neural network (1D-CNN) based structural damage assessment technique is validated with a benchmark study published by IASC-ASCE Structural Health Monitoring Task Group in 2003. In contrast with predominant machine learning based structural damage detection techniques of the literature, the technique shown in this paper runs without manual feature extraction or preprocessing stages. It runs directly on the raw vibration data. In CNNs, the stages of feature extraction and feature classification are merged into one stage; therefore, the proposed technique is efficient, feasible and economical. Utilizing the optimal features learned by 1D CNNs, the proposed CNN-based technique considerably improves the classification efficiency and accuracy. The performance improvement of the proposed technique is assessed by calculating the "Probability of Damage" values for damage estimations. The unseen structural damage cases between the two extreme end structural cases (zero damage and total damage) were successfully identified. Consequently, it is validated that the improved CNN-based technique is efficient since it predicted the level of damage consistently with the structural damage cases defined in the existing benchmark.