Structural Damage Detection Using Convolutional Neural Networks

Abstract

Detection of the deficiencies affecting the performance of the structures has been studied over the past few decades. However, with the long-term data collection from dense sensor arrays, accurate damage diagnosis has become computationally challenging task. To address such problem, this paper introduces convolutional neural network (CNN), which has led to breakthrough results in computer vision, to the damage detection challenge. CNN technique has the ability to discover abstract features which are able to discriminate various aspect of interest. In our case, these features are used to classify "damaged" and "healthy" samples modeled through the finite element simulations. CNN is performed by using a Python library called Theano with the graphics processing unit (GPU) to achieve higher performance of these data-intensive calculations. The accuracy and sensitivity of the proposed technique are assessed with a cracked steel gusset connection model with multiplicative noise. During the training procedure, strain distributions generated from different crack and loading scenarios are adopted. Completely unseen damage setups are introduced to the simulations while testing. Based on the findings of the proposed study, high accuracy, robustness and computational efficiency are succeeded for the damage diagnosis.