Seismic Health Monitoring of Foundations Using Artificial Neural Networks

Abstract

Damage identification plays an important role in structural health monitoring systems. Despite variety in damage identification methods, little attention has been paid to the seismic damage identification of foundations. When shear walls serve as the lateral load resistance system of structures, foundations may subject to the high level of concentrated moment and shear forces. Consequently, they can experience severe damage. Since such damage is often internal and not visible, visual inspections cannot identify the location and the severity of damage. Therefore, a robust method is required for damage localization and quantification of foundations. According to the concept of performance-based seismic design of structures, the seismic behavior of foundations is considered as Force-Controlled. Therefore, for damage identification of foundation, internal forces should be estimated during ground motions. In this study, for real-time seismic damage detection of foundations, a method based on artificial neural networks was proposed. A feed-forward multilayer neural network with one hidden layer was selected to map input samples to output parameters. The lateral displacements of stories were considered as the input parameters of the neural network while moment and shear force demands at critical points of foundations were taken into account as the output parameters. In order to prepare well-distributed data sets for training the neural network, several nonlinear time history analyses were carried out. The proposed method was tested on the foundation of a five-story concrete shear wall building. The obtained results revealed that the proposed method was successfully estimated moment and shear force demands at the critical points of the foundation.