Ultrasonic Point Cloud-Based Segmentation and Quantification of Internal Defects in Concrete Structures

Abstract

Accurate detection of internal defects in concrete structures is critical to structural safety. This study proposes a three-stage method for segmentation and geometric quantification of defects from 3D ultrasonic point clouds to support non-destructive testing (NDT). Sparse point clouds are densified by grid-based, acoustically constrained trilinear interpolation in the linear amplitude domain to preserve defect-boundary fidelity. An Amplitude-aware PointNet (AAPNet) integrates spatial coordinates with normalized and thresholded echo amplitude for defect segmentation. Connected-component analysis on voxelized defect regions enables estimation of length, width, height, and volume. Laboratory specimens containing voids, cracks, and delaminations were scanned with an Elop Insight 3D ultrasonic system. Segmentation was evaluated by nine-fold leave-one-specimen-out cross-validation (LOSO-CV) on 250 scans from nine independent single-defect specimens, yielding a mean IoU of 82.75 ± 2.66% (precision 92.03 ± 1.58%, recall 89.11 ± 1.73%, F1 90.54 ± 1.59%). Dimensional quantification on embedded defects gave relative volume errors of 2.6–14.9% and axis-aligned deviations within 2.0 cm.