Safety Assessment of Scaffolding on Construction Site using AI

• URL: http://arxiv.org/abs/2509.21368v1
• Date: Mon, 22 Sep 2025 14:43:20 GMT
• Title: Safety Assessment of Scaffolding on Construction Site using AI
• Authors: Sameer Prabhu, Amit Patwardhan, Ramin Karim,
• Abstract summary: This paper explores the use of Artificial Intelligence (AI) and digitization to enhance the accuracy of scaffolding inspection.<n>A cloud-based AI platform is developed to process and analyse the point cloud data of scaffolding structure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the construction industry, safety 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 the integrity and stability. At present, inspections are primarily visual and are conducted by 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 Artificial Intelligence (AI) and digitization to enhance the accuracy of scaffolding inspection and contribute to the safety improvement. A cloud-based AI platform is developed to process and analyse the point cloud data of scaffolding structure. The proposed system detects structural modifications through comparison and evaluation of certified reference data with the recent point cloud data. This approach may enable automated monitoring of scaffolding, reducing the time and effort required for manual inspections while enhancing the safety on a construction site.

Related papers

• DeepSight: An All-in-One LM Safety Toolkit [49.27916339801296]
  We propose an open-source project, namely DeepSight, to practice a new safety evaluation-diagnosis integrated paradigm.<n>DeepSight is low-cost, reproducible, efficient, and highly scalable large-scale model safety evaluation project consisting of a evaluation toolkit DeepSafe and a diagnosis toolkit DeepScan.<n>By unifying task and data protocols, we build a connection between the two stages and transform safety evaluation from black-box to white-box insight.
  arXiv  Detail & Related papers  (2026-02-12T15:43:14Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention [68.95008546581339]
  Existing decoding-time interventions, such as Contrastive Decoding, often force a severe trade-off between safety and response quality.<n>We propose CARE, a novel framework for decoding-time safety alignment that integrates three key components.<n>The framework achieves a superior balance of safety, quality, and efficiency, attaining a low harmful response rate and minimal disruption to the user experience.
  arXiv  Detail & Related papers  (2025-09-01T04:50:02Z)
• Are Large Pre-trained Vision Language Models Effective Construction Safety Inspectors? [0.0]
  Construction safety inspections typically involve a human inspector identifying safety concerns on-site.<n>With the rise of powerful Vision Language Models (VLMs), researchers are exploring their use for tasks such as detecting safety rule violations from on-site images.
  arXiv  Detail & Related papers  (2025-08-14T18:23:09Z)
• Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift? [51.12297424766236]
  AURORA is a framework to evaluate malware classifiers based on their confidence quality and operational resilience.<n>AURORA is complemented by a set of metrics designed to go beyond point-in-time performance.<n>The fragility in SOTA frameworks across datasets of varying drift suggests the need for a return to the whiteboard.
  arXiv  Detail & Related papers  (2025-05-28T20:22:43Z)
• Construction Site Scaffolding Completeness Detection Based on Mask R-CNN and Hough Transform [2.7309692684728617]
  This paper proposes a deep learning-based approach to detect the scaffolding and its cross braces using computer vision.<n>A scaffold image dataset with annotated labels is used to train a convolutional neural network (CNN) model.
  arXiv  Detail & Related papers  (2025-03-18T20:27:22Z)
• Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles [5.897549460175895]
  This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario.<n>It incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors.<n> Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection.
  arXiv  Detail & Related papers  (2025-03-13T04:13:48Z)
• AR-Facilitated Safety Inspection and Fall Hazard Detection on Construction Sites [17.943278018516416]
  We are exploring the potential of head-mounted augmented reality to facilitate safety inspections on high-rise construction sites.<n>A particular concern in the industry is inspecting perimeter safety screens on higher levels of construction sites, intended to prevent falls of people and objects.<n>We aim to support workers performing this inspection task by tracking which parts of the safety screens have been inspected.<n>We use machine learning to automatically detect gaps in the perimeter screens that require closer inspection and remediation and to automate reporting.
  arXiv  Detail & Related papers  (2024-12-02T08:38:43Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• UAV-based Visual Remote Sensing for Automated Building Inspection [15.471860216370251]
  Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters.
  arXiv  Detail & Related papers  (2022-09-27T14:18:14Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.