A bivariate Gaussian function approach for inverse cracks identification of forced-vibrating bridge decks

Abstract

This study deals with an inverse detection of stiffness degradation that occurs due to multiple cracks in bridge decks subjected to unknown moving loads. Six unknown parameters are considered to determine the damage distribution, which is a modified form of the bivariate Gaussian distribution function. The proposed approach is more feasible than the conventional element-based damage detection method from the computational efficiency because a finite element analysis coupled with a hybrid genetic algorithm using a small number of unknown parameters is performed. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the actual bridge modelled with a three-dimensional solid element. The numerical examples show that the proposed technique is a feasible and practical method, which can prove the location of a damaged region as well as inspect the distribution of deteriorated stiffness although there is a modelling error between actual bridge results and numerical model results as well as unknown moving loads.

Keywords:

• bivariate Gaussian function
• inverse cracks detection
• unknown moving load
• hybrid genetic algorithm
• dynamic response