Health monitoring method of large structures using neural networks technique

Abstract

This dissertation presents the practical techniques for structural health monitoring and damage identification of large civil infrastructures using neural networks technique. Neural networks techniques are employed in this study since they are computationally quick to estimate the damage severities for on-line monitoring and assessment and they can utilize various kinds of input data measured during the tests. It consists of two parts: i) development of a health monitoring method for bridges under ordinary traffic loadings using the neural networks technique and ii) enhancement of the damage estimation method for building frames using committee of neural networks. First, a method for structural health monitoring of bridges is presented including damage assessment based on ambient vibration data caused by the traffic loadings, and it is verified by tests on a bridge model. The procedure consists of the identification of the operational modal properties using the random decrement method, the updating of the baseline finite element model using the inverse modal perturbation technique, and the assessment of the damage locations and severities using the neural networks technique and the committee technique for neural networks. For the practical application of the structural health monitoring based on the vibration induced by operational loads, the sensitivity of the measured vibration data to changes in operating loads should be considered, and structural identification must be carried out with the changes of the modal parameters only due to the damages. In this study, as input to the neural networks for the damage estimation, the ratios of resonant frequencies between before and after the damages and the mode shapes after the damages are used. The frequency ratios have been used instead of the frequencies since the resonant frequencies of the bridge extracted from the vibration data vary depending on the mass of the moving vehicles. An experimental study is carried...