Structural Damage Detection Using Randomized Trained Neural Networks

TL;DR

This paper presents a neural network-based method for structural damage detection that estimates damage location and extent by analyzing changes in static parameters like strain and displacement, demonstrating improved accuracy with strain data.

Contribution

The work introduces a randomized training approach for neural networks to detect structural damage using static parameter changes, emphasizing strain over displacement for better accuracy.

Findings

Strain-based detection outperforms displacement-based methods.

Neural networks can recognize damage states with high accuracy.

Finite element analysis supports the effectiveness of the approach.

Abstract

A computationally method on damage detection problems in structures was conducted using neural networks. The problem that is considered in this works consists of estimating the existence, location and extent of stiffness reduction in structure which is indicated by the changes of the structural static parameters such as deflection and strain. The neural network was trained to recognize the behaviour of static parameter of the undamaged structure as well as of the structure with various possible damage extent and location which were modelled as random states. The proposed techniques were applied to detect damage in a simply supported beam. The structure was analyzed using finite-element-method (FEM) and the damage identification was conducted by a back-propagation neural network using the change of the structural strain and displacement. The results showed that using proposed method the strain is more efficient for identification of damage than the displacement.