Damage Detection and Localization in Structures: A Statistics Based Algorithm Using a Densely Clustered Sensor Network

Damage prognosis for structural health monitoring is a challenging and complex research topic in civil engineering. Early and accurate damage detection is essential to maximizing the useful life of structures. The use of densely clustered sensor networks provides promising applications in the analysis of structural components and identification of local damage. The proposed localized damage detection method utilizes a linear regression analysis to monitor changes in the linear behavior of a structure with the onset of damage. The structural responses at various sensor locations along a structure are compared to those of other locations and pair-wise influence coefficients are estimated. These coefficients serve as damage indicators when damaged values are compared to healthy-state values. By statistically comparing the change in influence coefficients, structural damage can be accurately and effectively identified. The method is verified using simulations and an experimental prototype of a local beam-column connection, as well as a simulated model of a twospan bridge girder. INTRODUCTION Structural health monitoring (SHM) plays an integral role in maintaining the integrity of important civil, mechanical, and aerospace engineering systems. Structures experience a number of dynamic influences on a daily basis ranging from typical ambient vibrations to more extreme wind and earthquake loadings. Whether the damaging effects of these load cases are visible immediately or appear more gradually in time, it is important to be able to detect the damage before it becomes detrimental to the entire structure and its surroundings. Not only will money be saved by being able to repair more manageable damages at earlier detection, but also fatal catastrophes can be prevented. As an increasing number of structures are beginning to reach the limit of their service lives, the need for effective, efficient, and affordable damage detection methods is becoming more apparent. Traditional non-destructive evaluation (NDE) techniques include but are not limited to visual inspection, liquid penetrant, magnetic particle, radiography, eddy 53 Structures Congress 2011 © ASCE 2011