Computational Inverse Technique in Nondestructive Detection of Flaws in Railway Tracks Using Genetic Algorithm

فاقد چكيده فارسي

Computational inverse techniques in conjunction with nondestructive methods have been proposed as potentially valuable tools for evaluating the substructure performance, determining locations along the track that require maintenance, and identifying appropriate solutions. In this paper, development of a Finite Element Analysis based on GA computing model is proposed for detecting uneven local settlement of ballast from simulated FWD data without the need for destructive field tests. For this purpose, the inverse problem is expressed as a minimization problem with the objective function being the mean square root of the differences between the measured quantities and the corresponding computed quantities from the assumed structural configuration. This objective function is used by the Genetic Algorithm (GA) to measure the fitness of individuals in a population of candidate solutions. The unknown parameter set to be identified is the reference loose sleeper number to indicate the location of settlement (in other words, reference number of springs with zero stiffness). The results indicate that the GA computational inverse technique has the potential to solve the inverse structural identification problems in a systematic and robust way.