Neural networks in Structural Health Monitoring

A Three Session Online Mini Course on Structural Health Monitoring
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Dear Colleagues,

I will be presenting a three-session mini course on Structural Health Monitoring Using Artificial Neural Networks and Comparison with Wavelet Analysis Approach, on Oct. 30, 31, and Nov. 1, 2012, as part of the Indo-US Collaboration for Engineering Education Virtual Academy Program.  Please encourage graduate students or anyone who might be interested in this topic visit:  id=811

2012-13 IUCEE Virtual Academy Semester One
Intro to MiniCourse, PG Audience
Structural Health Monitoring
Using Artificial Neural Networks and
Comparison with Wavelet Analysis Approach
Mohammad Noori

Abstract- Over the past two decades structural health monitoring (SHM) has emerged as a reliable and economical approach to monitor the system performance, detect damages if occurred, asses/diagnose the structural health condition, and make corresponding maintenance decisions. A SHM system consists of two major components: a network of sensors to collect the response data and data-mining algorithms to extract information on the structural health condition. If damage is detected, or the structural performance becomes unsatisfactory, appropriate control/maintenance actions can be taken. Extensive research has been carried out for the development of diagnostic methodologies and algorithms for real-time on-line application as an integral component of structural health monitoring systems. In many structural systems the restoring forces can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but require a priori knowledge of restoring force characteristics. Non-parametric approaches do not require this a priori information, but they typically lack direct associations between the model and the system dynamics, providing limited utility for health monitoring and damage detection. In this seminar an overview and comparison of various methodologies, such as wavelet transformation, for structural health monitoring is presented. Subsequently, a novel system identification approach, the Intelligent Parameter Varying (IPV) method, which is based on artificial neural networks, is presented and its application for identifying constitutive non-linearities in structures subject to seismic excitations are presented. IPV overcomes the limitations of traditional parametric and non-parametric approaches, while preserving the unique benefits of each. It uses embedded radial basis function networks to estimate the constitutive characteristics of inelastic and hysteretic restoring forces in a multi-degree-of-freedom structure. Simulation results are compared to those of a traditional parametric approach, the prediction error method. These results demonstrate the effectiveness of IPV in identifying highly nonlinear restoring forces, without a priori information, while preserving a direct association with the structural dynamics. This seminar will be of great value for graduate students and researchers involved in the field of structural health monitoring.

 

 

Source: Dr. Mohammad Noori

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