ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Structural Sensing, Control and Asset Management
This article is part of the Research TopicDigital Transformation in Construction: Integrating Metaverse, Digital Twin, and BIMView all 13 articles
AI-Driven Safety Assessment of Scaffolding using LiDAR Sensing
Provisionally accepted
- Luleå University of Technology, Luleå, Sweden
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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 to ensure both the reliability of assets and the safety of workers. Scaffolding, a key structural support asset, requires regular inspection to detect and identify alterations from the design rules that may compromise integrity and stability. At present, inspections are primarily visual and are conducted by the site manager or accredited personnel to identify deviations. However, visual inspection is time-intensive and can be susceptible to human errors, which can lead to unsafe conditions. This paper explores the use of AI and digital technologies to enhance the accuracy and efficiency of scaffolding inspection and contribute to the safety improvement. A cloud-based AI platform is developed to process and analyze 3D point cloud data of scaffolding structures, to detect modifications through comparison and evaluation of the certified reference scan with the recent scan. The proposed workflow incorporates concepts of Prognostics and Health Management (PHM) with continuous monitoring to identify structural modifications and further assist in decision-making. In doing so, it enables automated monitoring of scaffolding, reducing the time and effort required for manual inspections while enhancing the safety on a construction site.
Keywords: 3D point cloud analysis, artificial intelligence, Cloud-based monitoring platform, construction safety, Prognostic and health management, Scaffolding inspection
Received: 12 Oct 2025; Accepted: 05 Jan 2026.
Copyright: © 2026 Prabhu, Patwardhan and Karim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sameer Prabhu
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