A LiDAR-Based Automatic Workflow for Flatness Detection of Simulated Concrete Placing Surfaces Using Multi-Frame Median Fusion
Concrete placing surface flatness is an important quality indicator affecting thickness control, finishing efficiency, and subsequent construction quality. Although point-cloud-based flatness inspection has been widely studied for completed floors, walls, slabs, and precast components, near-real-time detection of small local unevenness on temporary concrete placing surfaces during construction remains insufficiently investigated, especially under the effects of point-cloud noise, local slope, incomplete coverage, and baseline false alarms. To address this gap, this study proposes a LiDAR-based automatic workflow that combines multi-frame median fusion, local reference-plane fitting, threshold-based deviation judgment, and visualized output. Indoor validation was conducted using an empty-ground baseline, 20 cm × 20 cm square plates, and 50 cm × 5 cm strip plates with thicknesses of 3, 5, and 10 mm. The average over-limit point ratio was 2.165% ± 0.192% for the empty-ground baseline. For 3, 5, and 10 mm square plates, the ratios were 2.536% ± 0.370%, 3.811% ± 0.638%, and 4.657% ± 0.850%, respectively; for strip plates, they were 2.269% ± 0.059%, 2.937% ± 0.443%, and 3.347% ± 0.435%, respectively. Compared with the baseline, the 5 and 10 mm square plates increased the ratio by 1.646 and 2.492 percentage points, while the 5 and 10 mm strip plates increased it by 0.772 and 1.182 percentage points. These results show that the workflow provides a clear thickness-dependent response for square targets and a detectable but weaker response for narrow strip targets. Scientifically, the study demonstrates how local threshold-scale unevenness can be distinguished from baseline point-cloud fluctuations. In application, it provides preliminary perception support for future online flatness inspection and automatic screeding assistance.