Comparison of Existing Damage Detection Algorithms on Structural Health Monitoring

Abstract eng:
This paper presents an operational assessment of existing damage detection algorithms for structural health monitoring (SHM) of building structures. This project assumed the typical SHM scenario involving continuous structural vibration recording using accelerometers. This study compared six established stiffness based damage detection algorithms utilising the ASCE benchmark instrumented structure dataset. This will be one of the first studies comparing the effectiveness of current damage detection algorithms using the benchmark data. The analyses indicate that all six considered damage detection algorithms have no difficulty in detecting the existence of the damage. Additionally, the identified stiffnesses using PP, FDD, EFDD methods were the most accurate for identifying the damage severity. The AR-ARX model was the least accuracy in estimating the stiffness amongst the six methods. All six algorithms were sensitive to modelling errors, while ANN and AR-ARX model techniques were sensitive to loading condition.

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