Safety risk factors comprehensive analysis for construction project: Combined cascading effect and machine learning approach

Guo, Ma; Wu, Zhijiang; Jia, Jianyao; Shang, Shanshan

doi:10.1016/j.ssci.2021.105410

Cited by 14 publications

(7 citation statements)

References 53 publications

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“…Analyzing "accidents" remains the dominant method [56]. "Machine learning" is gradually replacing traditional approaches [57][58][59]. "Wearable sensors" is increasingly apparent in addressing human risks [60][61][62], which can monitor the factors that may impact operation [63,64].…”

Section: Topic Trendsmentioning

confidence: 99%

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Junjia,

Alias,

Haron

et al. 2023

Buildings

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Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

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Section: Topic Trendsmentioning

confidence: 99%

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Junjia,

Alias,

Haron

et al. 2023

Buildings

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“…These tools bring advanced capabilities to project management, enabling tasks such as data analysis, risk assessment, decision-making, performance monitoring, and optimization [42], [49]. They leverage AI, machine learning, and statistical techniques to improve project planning, resource allocation, cost estimation, risk mitigation, and overall project outcomes [43], [52]. These tools enhance efficiency, accuracy, and insights in project management processes, ultimately leading to improved project success rates and delivery [50], [54]…”

Section: A Mapping the Ai Tools Implementation Challenges And Impacts...mentioning

confidence: 99%

Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review

Hashfi,

Raharjo

2023

IJACSA

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This paper presents a systematic literature review (SLR) investigating the challenges and impacts of implementing artificial intelligence (AI) in project management, specifically mapping them into the process groups defined in the Project Management Body of Knowledge (PMBOK). The study aims to contribute to the understanding of integrating AI in project management and provides insights into the challenges and impacts within each process group. The SLR methodology was applied, and a total of 34 scientific articles were analyzed. The results and analysis reveal the specific challenges and impacts within each process group. In the Initiating Process Group, AI tools and analysis techniques address challenges in risk assessment, cost prediction, and decision-making. The Planning process group benefits from various tools and methodologies that improve risk assessment, project selection, cost estimation, resource allocation, and decision-making. The Execution process group emphasizes the importance of advanced tools and techniques in enhancing productivity, resource utilization, cost reduction, and decision-making. The Monitoring and Controlling process group demonstrates the potential of advanced tools in achieving efficiency, cost reduction, improved quality, and informed decision-making. Lastly, the Closing process group emphasizes the importance of utilizing advanced tools to minimize waste, optimize resource utilization, reduce costs, improve quality, and project closure success. Overall, this research provides valuable insights and strategies for organizations seeking to implement AI in project management, thereby enhancing the potential for success within the PMBOK Process Group.

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“…Many methods have been utilized to study construction safety management. For example, Ma et al [32] used machine learning to synthesize construction safety risk factors. Yi and Wu [33] reviewed the application of combined artificial intelligence and big data for safety management in construction projects.…”

Section: Evolutionary Game Theorymentioning

confidence: 99%

Analysis of Behavioral Strategies of Construction Safety Subjects Based on the Evolutionary Game Theory

Sun

et al. 2022

Buildings

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In construction safety management practices, identifying key stakeholders and specifying their safety behavior patterns are critical to avoid unsafe practices, which are the major cause of construction accidents. This paper argues that the main contractor and construction workers are key safety stakeholders in the construction safety management in China. The purpose of this paper is to develop an evolutionary game model to explore the safety behavior strategies of the main contractor and construction workers. In this framework, we explored the effects of model parameter changes on the evolution of safety behavior strategies and verify the evolution paths under different initial values by numerical simulations. The findings of this paper are as follows: Firstly, the safety and stability of construction projects are related to the possible losses caused by accidents, the amount of work required by construction workers to comply with safety regulations, the gain of bravado, the probability of accidents, and cost savings from the main contractor’s lax supervision. Secondly, the role of restraining the unsafe behavior of construction workers through the form of fines is limited, and increasing the fines to the main contractor cannot effectively reduce the unsafe behaviors. Thirdly, strengthening safety education, creating a safe atmosphere, and optimizing operating procedures are effective measures to improve the effectiveness of safety behavior. This study further extends the application of evolutionary game theory to construction safety management, and the findings can provide clear guidance for the development of effective incentives and constraints on safety behavior.

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Safety risk factors comprehensive analysis for construction project: Combined cascading effect and machine learning approach

Cited by 14 publications

References 53 publications

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review

Analysis of Behavioral Strategies of Construction Safety Subjects Based on the Evolutionary Game Theory

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