Structural Impairment Detection Using Deep Counter Propagation Neural Networks

Abstract

Structural systems may deteriorate under normal operating conditions during their service life. In many situations, damage is not easily observed through traditional visual inspection techniques. The need for detecting subtle structural damage and assessing the level of impairment presents opportunities to develop innovative solutions beyond typical inspections. One method for monitoring and evaluating structures is to measure responses and assess the impairment condition of structures using those responses. This paper presents an inverse static assessment method for evaluating structures using this concept. Solutions to such an inverse problem are difficult; data streams from structural measurements may be noisy and sometimes incomplete. Additionally, finding an exact, explicit, closed-form solution to this problem is often impossible. This paper details a neural network approach to solve the inverse impairment detection problem. The approach presented utilizes a deep counter propagation neural network that is capable of modeling input-output functional relations even when mathematically explicit formulas are unavailable or data is noisy and/or corrupt.