Damage Diagnosis of Steel Truss Bridges under Varying Environmental And Loading Conditions

Abstract

In this paper, we propose a damage detection and localization algorithm for steel truss bridges using a data-driven approach under varying environmental and loading conditions. A typical steel truss bridge is simulated in ANSYS for data generation. Damage is introduced by reducing the stiffness of one or more members of the truss bridge. The simulated acceleration time-history signals are used for the purpose of damage diagnosis purpose. Vibration data collected from healthy bridges are processed through principal component analysis (PCA) to find the reduced size weighted feature vectors in model space. Unknown test vibration data (healthy or damaged) finds the closest match of its reduced size model from the training database containing only healthy vibration data. The residual error between the spread of closest healthy vibration data and unknown test vibration data is processed to determine damage location and severity of the damage to the structure. A comparative study between a proper orthogonal decomposition (POD) based damage detection algorithm and proposed algorithm is presented. The results show that the proposed algorithm is efficient to identify the damage location and assess the severity of damage, called as the Damage Index (DI), under varying environmental and moving load conditions.

Moving Principal Component Analysis Based Structural Damage Detection for Highway Bridges Under Moving Vehicles

“…Combining Eqs. ( 21) to (23), the instant tangent stiffness of the virtual rotational spring 𝐾 𝑟𝑑 is obtained.…”

“…The PCA has been used to separate the structural damage features from that by environmental changes and then the environmental effects are eliminated during the process of bridge condition assessment [22]. The PCA has been used to reduce the size of feature vectors [23], eliminate the operational and environmental effects [24,25] and reduce the noise effect [26] for structural damage detection. With the traditional PCA, the whole time series of measured responses is analysed and it cannot reflect the instant status of the structure [27].…”

Moving-Principal-Component-Analysis-Based Structural Damage Detection for Highway Bridges in Operational Environments

“…The PCA has been used to separate the structural damage features from those of the environmental changes, and then the environmental effects are eliminated during the process of bridge condition assessment [ 22 ]. The PCA has been used to reduce the size of feature vectors [ 23 ], eliminate the operational and environmental effects [ 24 , 25 ], and reduce the noise effect [ 26 ] for structural damage detection. With the traditional PCA, the whole time series of measured responses is analysed, and it cannot reflect the instant status of the structure [ 27 ].…”