Artificial intelligence-driven safety assessment of scaffolding using LiDAR sensing

The construction industry is embracing transformation through the integration of digitization, artificial intelligence (AI), and immersive technologies. On a construction site, continuous assessment is vital for ensuring both the reliability of assets and safety of workers. Scaffolding is a key structural support asset that requires regular inspections for detection and identification of alterations from the design rules that could compromise integrity and stability. At present, such inspections to identify deviations are primarily visual and conducted by the site managers or accredited personnel. However, visual inspection is time-intensive and susceptible to human errors, which can lead to unsafe conditions. This study explores the use of AI and digital technologies to automate and enhance scaffolding inspections process to contribute toward safety improvement. A cloud-based AI platform is developed to process and analyze 3D point-cloud data of scaffolding structures to detect modifications through comparisons as well as evaluate the certified reference scan with a recent scan. The proposed workflow incorporates prognostics and health management concepts with continuous monitoring to identify structural modifications and further assist with decision-making. The results indicate that the proposed approach can limit reliance on manual visual inspections. By enabling automated monitoring of scaffolding, the proposed approach reduces the time and effort required for inspection process, while enhancing the safety on a construction site.