An Energy-Based Damage Diagnosis Under Changing Environmental Temperature for Online SHM.

Structural health monitoring (SHM) for early diagnosis of damage in structures has become vital in improving the structural integrity and safety of the structure and thereby providing a smooth operation for most of the important in-service civil structures such as buildings and bridges. SHM also contributes to the substantial reduction in the maintenance cost of the structure. During serious structural failures, SHM can safeguard human life through an early warning system. This paper presents an output-only model-free technique by extracting damage features from the energy profiles derived from measured time-history responses. The proposed algorithm is organized into two phases i.e. the learning phase and the monitoring phase. While the learning phase can optionally be executed offline, the monitoring phase can preferably be online. The damage feature is extracted by carrying out a fractal dimension analysis of the continuous waveform of energy associated with the jolt (i.e. rate of change of acceleration) evaluated from the acceleration responses of the structure. A novel damage index is proposed, to localize the damage, using the damage features extracted from the fractal dimension, while handling operational and environmental variability (EoV). The proposed algorithm is evaluated through numerical simulations on a typical span of 220 m RCC bridge across river Amaravathi in Tamil Nadu, India and also a 20-story frame structure. Experimental investigations are also carried out by testing a laboratory-scale 10-story frame structure and also an RCC bridge model of a 7.7 m span to evaluate the practical amenability of the proposed algorithm. The experimental and numerical studies carried out in this paper, suggest that the proposed energy-based diagnostic technique can effectively localize the damage even in the presence of confounding factors like EoV. Conclusively, the proposed diagnostic technique can be an effective tool for online SHM, as it is model-free with less tedious computations and can perform well even in the presence of noise. [ABSTRACT FROM AUTHOR]