A neural network approach to nondestructive evaluation of complex structures, with application to highway bridges

Many methods have been proposed for nondestructive evaluation (NDE) of structures such as highway bridges, skyscrapers, and pipelines. The analysis of acoustic emission (AE) signals produced during cracking in concrete or steel is a promising approach for nondestructive monitoring to detect degradation in the integrity of a structure. Because of their central role in the highway infrastructure, bridge analysis is a particularly important application area for NDE. We discuss the advantages and disadvantages of AE testing, and describe some of the difficulties in applying classical signal processing (deconvolution) techniques to AE analysis of a bridge. We present instead a neural network approach that has the potential to overcome many of these difficulties.