Locating and Quantifying Damage in Deck Type Arch Bridges Using Frequency Response Functions and Artificial Neural Networks

Abstract

Vibration-based methods can be used to detect damage in a structure as its vibration characteristics change with physical changes in the structure. Arch bridge is a popular type of bridge with rather complex vibration characteristics which pose a challenge for using existing vibration-based methods to detect damage in the bridge. Further, its particular geometry with a curved arch rib and vertical members (either in compression or tension) to support the horizontal deck makes the process of damage quantification using vibration-based methods harder and challenging. This paper develops and presents a vibration-based method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to locate and quantify damage in the rib of deck-type arch bridge, which is the most important load bearing component in the bridge. Principal component analysis, which is performed to reduce the dimension of original FRF data series and to obtain limited principal component analysis (PCA)-compressed FRF data is used in the development of the proposed method. FRF change, which is the difference in the FRF data between the intact and the damaged structure, is compressed to a few principal components and fed to ANNs to predict the location and severity of structural damage. The process and the hierarchy of developed ANN systems are presented, including the “fusion network” concept, which individually analyses FRF-based damage indicators separated by sensor locations. Finally, results obtained for many tested damage cases (inverse problems) are presented, which demonstrate the applicability of the proposed method for locating and quantifying damage in the rib of deck type arch bridge.