This paper presents a novel approach for automatic, preliminary detection of damage in concrete structures using ground-based terrestrial laser scanners. The method is based on computation of defect-sensitive features such as the surface curvature, since the surface roughness changes strongly if an area is affected by damage. A robust version of principal component analysis (PCA) classification is proposed to distinguish between structural damage and outliers present in the laser scanning data. Numerical simulations were conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of superficial damage on the investigated region. The method provides a complete picture of the surface health of concrete structures. It has been tested on two real datasets: a concrete heritage aqueduct in Brooks, Alberta, Canada; and a civil pedestrian concrete structure. The experiment results demonstrate the validity and accuracy of the proposed systematic framework for detecting and localizing areas of damage as small as 1 cm or less.

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