A Robust Estimate Method for Damage Detection of Concrete Structures Using Contaminated Data

Abstract

Damage detection of concrete structures based on finite element model and measured response parameters has been an important research topic in recent years. It is well known that test data of mechanical behavior of concrete show great scatterness. As a result, the measured response parameters of concrete structures sometimes have gross errors. The gross error is a physical quantity that is much larger than data noise, which may lead to serious distortion of calculation results. To this end, a new robust estimate method termed as the augmented inverse estimate is proposed in this work for damage detection of concrete structures to resist gross errors in data. It has the advantages of very simple programming, convenient utilization, high computational accuracy, and broad prospect of application. Central to the augmented inverse estimate are the augmentation of coefficient matrix and the multiple computations based on feedback evaluation. A reinforced concrete beam structure is used as an example to verify the proposed method. It was found that the proposed method can successfully identify the location and extent of structural damage even if the used data have gross errors.