Structural model updating by numerical optimization and artificial neural network

Abstract

Changes of physical properties of structure (mass, stiffness and damping ratio) can be reflected on its dynamic characteristics. This project studied two approaches in structural model updating utilizing measured dynamic data: Numerical Optimization and Artificial Neural Network (ANN). Both numerical and experimental studies were carried out on a four-story shear building model under laboratory conditions for the demonstration and verification of these two approaches. In the experiment, vibration responses of a shear building model were measured, for the nominal case and 11 different additional mass cases, respectively. Then, modal parameters (i.e., natural frequencies and respective mode shapes) were identified from the measured vibration responses by the method of MODE-ID (Beck 1978). In each additional mass case, different masses were added to different floors of the shear building model. This project aims in studying the performance of the two approaches in identifying the location and magnitude of the added mass(es). For the purpose of structural model updating, the two approaches were implemented separately. One is numerical optimization, which is to establish an analytical model to match the measured dynamic structural response by minimizing the discrepancy between the experimental measured and model-predicted modal parameters, such as natural frequencies and mode shapes. The other approach is ANN, which is a network of processing units. In this study, an ANN is trained by using the measured modal parameters as inputs and the corresponding structural parameters (i.e., mass at different floor of the shear building model in this study) as the outputs. Computer simulated data was employed to train the ANN and experimental measured modal parameters were used to test the performance of the trained ANN in estimating the mass distribution of the building.