Abstract
In the agricultural field, concrete headworks is the most important structure for the irrigation system. In recent years, a number of agricultural concrete infrastructures aging for a long-term period have been increasing. For maintenance and management, conventional inspection methods are time-consuming and costly, such as the electromagnetic wave method and elastic wave method. The detection of surface damage is more effective, safe and reliable than before since the laser scanning method provides detailed geometric information about the structure. The fundamental studies on point cloud data have been conducted in the civil engineering fields; nevertheless, the characteristics of point cloud in agricultural infrastructures, such as dam, headworks and canal, have not been discussed. In this study, 3D point clouds are generated for a concrete irrigation structure using the laser scanning method. The characteristics of surface damage which are quantitatively evaluated using point cloud information, geometric information and intensity parameter are investigated. The types of detected damage are efflorescence and cracks. It is investigated whether point clouds generated from a single scan or multiple scans are more effective for highly accurate detection. The characteristics of surface damage are evaluated by geometric features. The distance between the fitted plane and points is calculated by RANSAC algorithm and roughness parameter. The amount of efflorescence is detected by the distance between the fitted plane from RANSAC algorithm and points. The crack is detected by the local plane fitting method. The types of damage are characterized by the intensity parameter which is related to the color, roughness and moisture of the object. The surface damage and condition are evaluated by both geometric information and intensity parameter. These results show the unique parameters of point clouds from laser scanning methods, such as geometric features and intensity parameter, are useful to evaluate the characteristics of surface damage.
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Shibano, K., Morozova, N., Ito, Y. et al. Evaluation of surface damage for in-service deteriorated agricultural concrete headworks using 3D point clouds by laser scanning method. Paddy Water Environ 22, 257–269 (2024). https://doi.org/10.1007/s10333-023-00965-3
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DOI: https://doi.org/10.1007/s10333-023-00965-3