000			02217aab a2200229 4500
008			240216b20232023|||mr||| |||| 00| 0 eng d
022			_a0733-9364
100			_aMostofi, Fatemeh _9878741
100			_aTogan, Vedat _9878743
245			_aA Data-Driven Recommendation System for Construction Safety Risk Assessment
300			_a1-14 p.
520			_aSubjectivity and uncertainty of risk assessment (RA) procedures can be improved by replacing guesswork with data-driven approaches such as machine learning (ML). Although a plethora of ML prediction techniques have been introduced to improve the reliability of RA procedures, the utilization of ML-based recommendation systems that can leverage data from multiple aspects has remained unexplored. In this study, a novel RA recommendation system (RARS) was developed to achieve more reliable, objective, and inclusive safety decisions that can prioritize hazard items and formulate related risky scenarios. To this end, a semisupervised graph representation learning framework, node2vec, was utilized to receive semantic and dependency information from safety records to recommend the components of potential accident scenarios (hazards, hazardous cases, dangerous activities, and risky behaviors) based on the given decision objective. The RARS’s ability to provide flexible and user-oriented safety recommendations was explored on a real-life construction accident data set. This allows construction safety practitioners to dynamically evaluate possible risky scenarios with details regarding different influential risk factors and accordingly devise more reliable site safety strategies and relevant policies.
650			_aConstruction Safety Management _9879225
650			_aRisk Assessment (RA) _9880991
650			_aNode2vec _9878746
650			_aGraph Representation Learning _9880992
650			_aRecommendation System _9683077
650			_aData-Driven Decision-Making _9880993
773	0		_tASCE: Journal of Construction Engineering and Management _x07339364 _dReston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
856			_uhttps://doi.org/10.1061/JCEMD4.COENG-13437
942			_2ddc _n0 _cART _o14993 _pMr. Muhammad Rafique Al Haj Rajab Ali (Late)
999			_c814993 _d814993