A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles

摘要

A genetically optimized neural detector is utilized for the identification of structural defects in concrete piles. The proposed methodology is applied on numerically generated data, involving two major defect types. A coupled finite element and scaled boundary finite element method approach is used to model the pile and its surrounding soil. The oscillation patterns, produced on the surface of the pile, depend strongly on the introduced defect type. The proposed defect detection system provides information about the type and the placement of the defect(s), given the surface's oscillation patterns.