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Construction accidents pose significant risks to workers and the public, affecting industry productivity and reputation. While several reviews have discussed risk assessment methods, recent advancements in artificial intelligence (AI), big data analytics, and real-time decision support systems have created a need for an updated synthesis of the quantitative methodologies applied in construction safety. This study systematically reviews the literature from the past decade, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough search identified studies utilizing statistical analysis, mathematical modeling, simulation, and artificial intelligence (AI). These methods were categorized and analyzed based on their effectiveness and limitations. Statistical approaches, such as correlation analysis, examined relationships between variables, while mathematical models, like factor analysis, quantified risk factors. Simulation methods, such as Monte Carlo simulations, explored risk dynamics and AI techniques, including machine learning, enhanced predictive modeling, and decision making in construction safety. This review highlighted the strengths of handling large datasets and improving accuracy, but also noted challenges like data quality and methodological limitations. Future research directions are suggested to address these gaps. This study contributes to construction safety management by offering an overview of best practices and opportunities for advancing quantitative risk assessment methodologies. [ABSTRACT FROM AUTHOR]

Multicriteria analysis is widely used to prove the excellence of prefabricated construction compared with conventional construction. However, because previous studies have not presented the results of an integrated analysis, identifying the merits of prefabricated construction is challenging. Furthermore, clients experience difficulty when considering prefabricated construction owing to the complexity of simulations and the lack of data. Therefore, this study aimed to conduct a multicriteria analysis for prefabricated construction considering productivity, safety, environment, and economy, and develop a multi-prediction model. This study was conducted in five stages. Results revealed that prefabricated construction was superior to conventional construction for all variables, with the former scoring 0.0927 on average and the latter scoring 1.863. The multiprediction model utilizing a decision tree and Bayesian optimization has a high performance, achieving over 94%. Using study findings, decision makers can use the multiprediction model to assess the expected performance of prefabricated construction. This enables a comprehensive comparison of various conditions across different aspects through the multicriteria analysis. [ABSTRACT FROM AUTHOR]

Off-site construction is well-known technology that facilitates parallel processes of manufacturing and construction processes. This method enhances productivity while reducing accident, cost, and environmental impact. Many studies have highlighted its benefits, prompting further encouragement of off-site construction. This study consolidates current research and charts future directions by reviewing the existing literature. However, reviewing papers is time-intensive and laborious. Consequently, generative AI models, particularly Large Language Models (LLMs), are increasingly employed for document summarization. Specifically, LangChain influences LLMs through chaining data, demonstrating notable potential for research paper reviews. This study aims to evaluate the well-documented advantages of off-site construction through LangChain integrated with an LLM. It follows a streamlined process from the collection of research papers to conducting network analysis, examining 47 papers to uncover that current research primarily demonstrates off-site construction's superiority through cutting-edge technologies. Yet, a data deficiency remains a challenge. The findings demonstrate that LangChain can rapidly and effectively summarize research, making it a valuable tool for literature reviews. This study advocates the broader application of LangChain in reviewing research papers, emphasizing its potential to streamline the literature review process and provide clear insights into off-site construction's evolving landscape. [ABSTRACT FROM AUTHOR]

The construction industry records higher accident rates than other industries, and thus, risk estimation is necessary to manage accident rates. Risk levels differ based on facility type and construction project size. In this sense, this study aims to calculate the quantitative accident risk level according to the construction project size per infrastructure facility type. To this end, the following five-step risk estimation was performed: (1) data collection and classification; (2) calculation of fatality rate based on construction cost; (3) calculation of fatal construction probability by construction cost classification; (4) reclassification of construction cost considering fatal construction probability; and (5) calculation of risk level by facility type and construction cost classification. As a result, the fatality rate per facility type was the highest in 'Dam' at 0.01024 (person/USD million). Additionally, the risk level according to the construction project size per facility type was the highest for 'Dam' (0.00403 person/USD million) for a construction of less than USD 0.77 million. The risk level presented in this study can be utilized as basic data in the design stage for safety management. Our results also indicate the necessity of preparing a separate construction cost classification for safety management. [ABSTRACT FROM AUTHOR]

All over the world, construction accidents and respiratory diseases are among the most important problems. The prefabricated system is an introduction to reduce emissions and accidents during the construction phase. However, there is a lack of research that proves the superiority of the prefabricated system in terms of both dust health impacts and accident risks simultaneously. Therefore, this study conducts an assessment in terms of dust health impacts and accident risks to compare the prefabricated system and the conventional system. This research is carried out in the following steps. (i) collection of data, (ii) calculation of dust emission, (iii) health impact assessment, and (iv) calculation of accident risk. The conventional system produced 5,454,527.43 kg of dust, had a willingness to pay $12,631.40, and caused 4.87 × 102 injuries and 8.05 × 102 fatalities, while the prefabricated system produced 2,711,423.72 kg of dust, had a willingness to pay $6282.02, and caused 3.45 × 102 injuries and 5.69 × 102 fatalities. The results show that when the prefabricated system is applied instead of the conventional system, the dust emission, health impact, and risk level can be reduced by 38.59%, 43.04%, and 29.03%, respectively. This study contributes to previous knowledge when decision-makers select prefabricated systems over conventional systems. Furthermore, it provides insights into the health and safety impacts of both construction methods and the necessary measures to mitigate these impacts. [ABSTRACT FROM AUTHOR]