Improved Quantitative Analysis of Concrete Defect Volumes Using Enhanced Point Cloud Slicing Methodology

Currently, concrete structural damage detection primarily relies on industrial cameras to capture 2-D images for identifying cracks and voids. However, assessing the extent of damage using these images is challenging. The existing volume measurement methods face significant errors and poor robustness due to irregularities in concrete defect formation in real-world scenarios. To address these challenges, this article proposes a quantitative analysis method for concrete defect volumes based on an improved point cloud slicing technique. Initially, high-precision 3-D cameras capture local point cloud data of concrete. Using a fusion method that integrates local curvature and relative distance, the areas showing defects in the concrete point cloud were successfully extracted. Subsequently, these defect areas undergo slicing, and an enhanced Euclidean clustering method effectively separates the contour boundaries of each slice. Further refinement involves combining the ray parity method and area effectiveness principle to accurately differentiate containment relationships among multicontour regions and calculate precise areas for each slice. By summing the areas of all slices, the volume of concrete defects is accurately determined. This article validates the proposed method’s accuracy and robustness in volume calculations through comparative experiments on standard specimens with different features and performs quantitative analysis of concrete defect volumes in real-world scenarios. Experimental results demonstrate that the proposed method outperforms the existing approaches in volumetric quantification of concrete defect point clouds, with an average measurement error of 3.08%. This represents a significant improvement over current methods and exhibits high robustness, providing a promising solution for evaluating the extent of concrete defect damage.