Automating scaffold safety inspections using semantic analysis of 3D point clouds

To prevent safety accidents caused by scaffolds, safety managers on-site need to check a list of regulations whenever scaffolds are assembled, used, and disassembled. However, numerous errors can result from conventional manual inspection methods, leading to potential safety accidents at construction sites. This paper presents a three-step methodology to automate the inspection process of scaffolds with minimum human intervention: 1) acquisition of point cloud data from a construction site using a Terrestrial Laser Scanner (TLS), 2) classification of each point into seven different elements using a deep learning-based 3D segmentation model, RandLA-Net, and 3) inspection of the required regulations using a robust regulation checking algorithm. The efficacy of this methodology was proven by validating two construction sites that were different from the training dataset, showing 100% and 76.5% regulation checking F2 scores, respectively. •This paper presents a methodology to automate the inspection process of scaffolds using TLS-acquired point clouds.•Point clouds were segmented into 7 classes using a deep learning algorithm.•A robust regulation checking algorithm is constructed to find violations in the data.•Experiments showed regulation checking F2 scores of 100% and 76.5% on two sites, respectively.