Corroboration of NDT and deconvolution neural networks for pedestrian bridge health assessment

Abstract

This paper describes the specific application of the non-destructive testing methods of visual inspection and ground penetrating radar (GPR) to a pedestrian bridge in Izmir, Turkey. The paper concentrates on the implementation of a deconvolution neural network (DNN) which is a procedure that employs neural network algorithms. By introducing collected GPR data to the DNN, the existence and location of cracks, rebar and moisture ingress on pedestrian pathways can reliably be located, thus providing superior information on which decisions relating to the functionality and life expectancy of a structure can be formulated. This study will be of benefit to engineers in providing a detailed and dependable assessment of the current state of structures such as pedestrian bridges.