The approach was verified on four cracked concrete specimens. The strategy is to first construct a low-resolution representation of the point cloud, then detect and localize anomalies, and finally construct a high-resolution representation around these anomalies to enhance their characterization. The architecture of the WNN is based on a single-layer neural network consisting of Mexican hat wavelet functions. The adaptive WNN is designed to sequentially self-organize and self-adapt in order to construct an optimized representation. This paper proposes a novel adaptive wavelet neural network (WNN)-based approach to compress data into a combination of low- and high-resolution surfaces, and automatically detect concrete cracks and other forms of damage. Major challenges in using this technology are in storing significant amount of data and extracting appropriate features enabling condition assessment. Terrestrial laser scanners (TLS) are promising for automatically identifying structural condition indicators, as they are capable of providing coverage for large areas with accuracy at long ranges. Current practices rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field, which are labor intensive, subject to personal judgment and experience, and prone to error. Objective, accurate, and fast assessment of civil infrastructure conditions is critical to timely assess safety risks.
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