Automated Verticality Quality Assessment in Prefabrication Construction Using Laser Scanning and Synthetic Point Cloud

Abstract

Laser scanning is increasing used as an efficient and accurate way for quality assessment for prefabrication elements. However, previous studies have faced challenges in automating point cloud processing due to reliance on manual parameter tuning for individual cases. To address this gap, this study presents a method for automating the verticality assessment of prefabricated buildings by leveraging laser scanned point cloud data in combination with synthetic datasets. The experiments establish reference parameters and suggestions for adjustment patterns under varying conditions, offering practical guidance for implementation in real projects. The reference parameters were validated on real datasets collected using three terrestrial laser scanners, Leica BLK360, FARO Focus X330 and Leica RTC360, without additional tuning. Consistent results across scanners confirmed the robustness and transferability of the parameters, with the automated checks identifying the same defective wall panels as manual inspections. The findings demonstrate that the proposed method is both effective and accurate while significantly reducing the need for manual intervention. In addition to verticality assessment, the proposed approach provides a general framework that can be extended to other automated quality checks. It can be applied across different projects and adapted to assess other geometric criteria such as flatness, horizontal alignment, and overall geometric assessment.