A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles. (1st January 2016) - Protopapadakis, Eftychios; Schauer, Marco; Pierri, Erika; Doulamis, Anastasios D.; Stavroulakis, Georgios E.; Böhrnsen, Jens–U.; Langer, Sabine
Highlights: We numerically simulate low strain integrity tests. Simulation uses a coupled finite/scaled boundary finite element method. Defect(s) recognition is based on topologically optimized neural networks. An island genetic algorithm optimizes the network topology. Feature extraction is based on noise modeling approaches. Abstract: 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.