Damage detection in a reinforced concrete slab using outlier analysis

Detecting damages from a global perspective, although still a topic of active research, has been resolved with reasonable success by adapting approaches from the fields of statistical pattern recognition and machine learning. As a result, substantial attention is now addressed to bridging the gap between research and industrial use, and numerous successful application studies have been reported for different types of engineering structures. The present paper follows along this path and contributes with an experimental study concerning damage detection in a reinforced concrete slab based on conventional outlier analysis of captured vibration response. The concrete slab is investigated in five measurement setups; one reference, with an initial dent, and four damaged states in which the dent is extended by blank shots fired into the surface. In each state, the structure is subjected to controlled impulse excitation from an installed electro-mechanical actuator, and the response is captured by 14 accelerometers distributed evenly over the concrete slab. The unique entries in the covariance matrix of these accelerations are employed as the feature in the outlier analysis, where the damage index is the Mahalanobis metric.