Evaluating two model-free data interpretation methods for measurements that are influenced by temperature
Interpreting measurement data to extract meaningful information for damage detection is a challenge for continuous monitoring of structures. This paper presents an evaluation of two model-free data interpretation methods that have previously been identified to be attractive for applications in structural engineering: moving principal component analysis (MPCA) and robust regression analysis (RRA). The effect of three factors are evaluated: (a) sensor-damage location, (b) traffic loading intensity and (c) damage level, using two criteria: damage detectability and the time to damage detection. In addition, the effects of these three factors are studied for the first time in situations with and without removing seasonal variations through use of a moving average filter and an ideal low-pass filter. For this purpose, a parametric study is performed using a numerical model of a railway truss bridge. Results show that MPCA has higher damage detectability than RRA. On the other hand, RRA detects damages faster than MPCA. Seasonal variation removal reduces the time to damage detection of MPCA in some cases while the benefits are consistently modest for RRA.
damage detection damage detection moving principal component analysis robust regression analysis time to damage detection damage detectability seasonal temperature variation