Data-driven network analysis of construction hazard characteristics and warning strategy
LIU Mei1, XU Linyu2, LIAO Pin-Chao2, HUANG Yuecheng2, SUN Chengshuang1
1. School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2. Department of Construction Management, Tsinghua University, Beijing 100084, China
Abstract：[Objective] The investigation and management of construction hazards have transformed with the digitization and intellectualization of the construction industry. However, the efficient analysis and application of large-scale and high-dimensional hazard data remain farfetched. In this study, a hazard characteristic network is constructed by extracting and reducing the dimensions of the hazard record data on the construction site and data-driven hazard warning strategies are preseneted. [Methods] First, the hazard records are standardized using the text mining method and 111 safety hazard characteristics are extracted. Second, the correlations of these characteristics are determined using the correlation test, and the characteristic network is established based on the correlation strength calculation. Third, critical hazard characteristics are identified based on the analysis of the structural and individual indices in the network. In addition, 11 groups are obtained by the hierarchical clustering of all hazard characteristics. Finally, based on the above methods and analyses, data-driven warning strategies are preseneted in view of hazard characteristics. [Results] Through charateristic extraction and hierarchical clustering of potential safety hazards, 111 potential safety hazard characteristics and 11 potential safety hazard characteristic groups are determined to reduce the dimensions of potential safety hazard data. Based on the correlation test and correlation strength calculation, a safety hazard characteristic network is constructed, and the network and individual indices of safety hazard characteristics (out-degree, in-degree, intermediate, and eigenvector centralities) are analyzed to identify the key hazard characteristics (“facilities/equipment/apparatus/devices”, “settings”, “scaffolding”, and “railings”) and their associated paths with different roles. The early-warning strategies based on the data-driven network analysis are presented to improve timeliness from two aspects. In the early-warning process, relevant potential hazard information and importance ranking are obtained through the correlation analysis of the safety hazards via onsite inspection. Under limited manual labor, the safety management personnel are equipped with troubleshooting ideas and clues to mitigate the limitations of the original unplanned hazard inspection method. Meanwhile, a programmed information system can be further developed to provide early-warning tools. Safety managers can input the hazard information, and the system will conduct rapid standardization in the background to deal with the information and provide the associated hazards and sequencing to ensure timely feedback of hazard inspection clues. [Conclusions] This study establishes the standardization and characteristic extraction method of hazard record data which reduces data dimensionality from 3 [KG-*7]267 nonstandard hazard records to 111 hazard characteristics and 11 hazard characteristic groups to clarify the key information regarding inspection and governance, such as hazard types and scenarios. Based on the analysis of individual and structural indices of the hazard characteristic network, the deduction and importance ranking of potentially associated hazard characteristics are realized, thus providing early-warning clues for effective safety inspection and governance. The early-warning strategies based on data-driven hazard characteristics can not only address the inefficient original unscheduled search method in the early-warning process but also improve safety management efficiency through timely feedback of inspection clues. This study introduces a foundational method for mining regular strategy information with early-warning clues of engineering construction safety hazard data, as well as for effective inspection and governance of construction safety hazards.
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2023, Vol. 63 
