Automated Safety Risk Assessment Framework by Integrating Safety Regulation and 4D BIM-Based Rule Modeling

Autor: Dohyeong Kim, Taehan Yoo, Si Van-Tien Tran, Doyeop Lee, Chansik Park, Dongmin Lee
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Buildings, Vol 14, Iss 8, p 2529 (2024)
Druh dokumentu: article
ISSN: 2075-5309
DOI: 10.3390/buildings14082529
Popis: Performing risk assessments in construction requires collecting and analyzing project data and historical safety accident data, which is challenging due to the inherent complexities and dynamic nature of construction projects. To address these challenges, building information modeling (BIM) has been leveraged as a centralized digital repository that integrates data and provides a holistic 3D view of a project. Previous studies have highlighted BIM’s significant functions for risk assessment, such as visualization, simulation, and clash detection. However, these studies often overlook the incorporation of temporal information, which is crucial for assessing risks accounting for the dynamic conditions of construction sites. This study develops a 4D BIM-based risk-assessment framework by integrating spatial and temporal data to respond to dynamic site changes. The framework leverages 4D BIM to combine 3D model data with time-, resource-, and logistics-related information, enhancing the tracking and evaluation of construction progress. The study involves investigating major construction accidents, classifying their risk factors, establishing risk-factor identification algorithms, and implementing the framework on a web-based platform for validation. This approach offers a comprehensive risk-identification strategy, applicable to multiple accident types, with intuitive visualization using BIM models, benefiting from managers’ experiential knowledge and enabling effective risk assessments and mitigation strategies. Consequently, potential safety risks at construction sites can be efficiently identified using interconnected spatial and temporal data while tracking changes in risk levels in real time and visualizing them on a web-based platform.
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