Safety leading indicators in construction: A systematic review

Xu, Jing; Cheung, Clara; Manu, Patrick; Ejohwomu, Obuks

doi:10.1016/j.ssci.2021.105250

Cited by 35 publications

(19 citation statements)

References 98 publications

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“…To ensure the implementation and completion of a megaproject, it must be supervised and managed during this stage. Therefore, investments and resources are considered the major nodes for achieving the target of megaproject management, including conflict management [53], quality management [54,55], security management (safety management) [56][57][58][59], and schedule management [42,60,61]. Significantly, qualified management strategies and measures result in successful megaprojects.…”

Section: Overview Of Megaproject Management Researchmentioning

confidence: 99%

Megaproject Management Research: The Status Quo and Future Directions

Yuan

Wang

et al. 2021

Buildings

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Megaproject practices worldwide have triggered increasing research in megaproject management issues and led to an increasing number of papers being published during the last decade. However, it is demonstrated by the literature that there is no systematic examination on research development in the discipline of megaproject management, and consequently it is very difficult for scholars to quickly understand and grasp the research trend. Therefore, a research question naturally comes out, i.e., what is the status quo of megaproject management research and what are the research directions worthy of further investigation? This study aims to answer the question by conducting a systematic examination of the research development in the discipline of megaproject management. A total of 117 relevant articles, identified from six major international journals between 2009 and 2021, were analyzed based on the number of papers published annually, main author contributions, citations, categorization of the research methods and data analysis methods adopted, and research topics covered. The results indicated that developed countries, such as Australia, Canada, the United States, and the United Kingdom, have enjoyed significant advantages in terms of megaproject management research. It also revealed that more sophisticated views and theory have been used effectively, rather than only basic qualitative methods, in a number of studies on megaproject management. Future studies on megaproject management will be led globally, where megaprojects will remain designed and built to better built environments. In addition, continuous in-depth research on related topics can promote innovation in megaproject management to achieve sustainable megaproject development. Megaproject management will continue to be a hot research topic in the future; in particular, megaproject investment and finance management have emerged as new challenging topics. The findings can be valuable for both industry practitioners and researchers to gain deeper understanding of the current status and future directions of megaproject management research.

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Section: Overview Of Megaproject Management Researchmentioning

confidence: 99%

Megaproject Management Research: The Status Quo and Future Directions

Yuan

Wang

et al. 2021

Buildings

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“…Conversely, fragmented and negative perceptions indicate a 'weak' safety culture, and an increased susceptibility to accidents (Clarke, 2000;Guldenmund, 2000;Health and Safety Commission, 1993;Singer et al, 2009). Indeed, safety culture is often referred to as a leading indicator (Choudhry et al, 2007;Xu et al, 2021) or a distal antecedent of safety (Beus et al, 2016), where safety culture has an indirect effect on accidents/injuries via safety-related behaviours (Zohar, 2003).…”

Section: Introductionmentioning

confidence: 99%

Understanding safety culture and safety citizenship through the lens of social identity theory

Tear

Reader²

2023

Safety Science

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“…Globally, the construction sector is infamous for high Occupational Health and Safety (OHS) issues (Manu et al, 2019). In this regard, a proactive safety management approach relying on learning from near-miss events instead of accidents and injuries is deemed necessary (Bugalia et al, 2021;Xu et al, 2021). Occupational Safety and Health Administration (OSHA) (https://www.osha.gov/incident-investigation) in the United States defines nearmisses as incidents in which a worker might have been hurt if the circumstances had been slightly different.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based automated classification of worker-reported safety reports in construction

Bugalia¹,

Tarani²,

Kedia³

et al. 2022

ITcon

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Limited academic attention has been paid to the applicability of Machine Learning (ML) approaches for analyzing worker-reported near-miss safety reports, as opposed to injury reports, at construction sites. Although resource-efficient analysis through ML of large volumes of such data at construction sites can help guide practitioners in decision-making to prevent injuries. The current study addresses this research gap by evaluating the relevance of ML approaches through quantitative and qualitative methods for scaling efficient near-miss reporting programs at construction sites. The study uses an extensive experimentation strategy consisting of input data processing, n-gram modeling, and sensitivity analysis. It first tests the proposition that, despite the data-quality challenges, the high performance of different ML algorithms can be achieved in automatically classifying the textual near-miss observations. The study relies on worker-reported near-miss data collected from a real construction site in Kuwait. The classification performance of various ML approaches is evaluated using F1 scores for three academically novel but commonly used category labels at the sites - "Unsafe Act (UA)," "Unsafe Condition (UC)," and "Good Observation (GO)." In addition, the practitioner's input was utilized to assess the practical applicability of ML classifiers for construction sites. The conventional Logistic Regression (LR) classifiers have a comparatively high F1 score of 0.79. However, ML classifiers faced challenges in distinguishing between UA and UC. Further, the analysis reveals that optimal ML classifiers may lose on being acceptable to human decision-makers. Overall, despite the promising performance of ML tools for the near-miss data, the sites with low maturity of reporting systems may find themselves unable to leverage ML to scale their reporting systems. A simplified experimentation strategy like the current study could help practitioners identify the data-specific optimal ML approaches in future applications.

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Safety leading indicators in construction: A systematic review

Cited by 35 publications

References 98 publications

Megaproject Management Research: The Status Quo and Future Directions

Megaproject Management Research: The Status Quo and Future Directions

Understanding safety culture and safety citizenship through the lens of social identity theory

Machine learning-based automated classification of worker-reported safety reports in construction

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