Methodologies for model-free data interpretation of civil engineering structures

Structural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation. This paper presents two methodologies for model-free data interpretation to identify and localize anomalous behaviour in civil engineering structures. Two statistical methods based on (i) moving principal component analysis and (ii) robust regression analysis are demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The methodologies are tested on numerically simulated elements with sensors for a range of noise in measurements. A comparative study with other statistical analyses demonstrates superior performance of these methods for damage detection. Approaches for accommodating outliers and missing data, which are commonly encountered in structural health monitoring for civil structures, are also proposed. To ensure that the methodologies are scalable for complex structures with many sensors, a clustering algorithm groups sensors that have strong correlations between their measurements. Methodologies are then validated on two full-scale structures. The results show the ability of the methodology to identify abrupt permanent changes in behavior.