Structural damage fault detection using Artificial Neural network profile monitoring

Abstract

In today's world, structural development with reliability and integrity is an ever demanding process. Fault detection is the identification of normal healthy behavior of a system or process and recognition of any deviation from such normal behavior. Fault detection in structural systems provides important liability and financial advantages since it gives the decision-makers lead-time and flexibility to manage the health of the system. Structural systems are critical systems that require continuous monitoring of damage accumulation caused by earthquake loads that may cause catastrophic failures. We present in this research a data-driven methodology for fault detection of structural systems using multivariate statistical process control (MVSPC). The proposed method based on modeling overall structural damage using artificial neural networks (ANN) as a function of the earthquake load intensity. Hotelling T2 technique is then used to identify any shifts of the ANN model weights from their healthy states. The proposed method is tested and validated using simulation data fora four-story RC building with varying concrete strengths. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health via an inspection check to anticipate and potentially avoid failures.