A Data-Driven Recommendation System for Construction Safety Risk Assessment (Record no. 814993)

MARC details
000 -LEADER
fixed length control field 02217aab a2200229 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240216b20232023|||mr||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0733-9364
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Mostofi, Fatemeh
9 (RLIN) 878741
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Togan, Vedat
9 (RLIN) 878743
245 ## - TITLE STATEMENT
Title A Data-Driven Recommendation System for Construction Safety Risk Assessment
300 ## - PHYSICAL DESCRIPTION
Extent 1-14 p.
520 ## - SUMMARY, ETC.
Summary, etc. Subjectivity 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Construction Safety Management
9 (RLIN) 879225
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Risk Assessment (RA)
9 (RLIN) 880991
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Node2vec
9 (RLIN) 878746
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Graph Representation Learning
9 (RLIN) 880992
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Recommendation System
9 (RLIN) 683077
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data-Driven Decision-Making
9 (RLIN) 880993
773 0# - HOST ITEM ENTRY
Title ASCE: Journal of Construction Engineering and Management
International Standard Serial Number 07339364
Place, publisher, and date of publication Reston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1061/JCEMD4.COENG-13437">https://doi.org/10.1061/JCEMD4.COENG-13437</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Articles
-- 14993
-- Mr. Muhammad Rafique Al Haj Rajab Ali (Late)
Holdings
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  Periodical Section Vol.149, No.12 (December 2023)   16/02/2024 Articles