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The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

The construction industry has been recording a high number of accidents without any sign of abating, the majority of which have been linked to poor health and safety practices. To reduce this menace, this study aims to evaluate the factors affecting health and safety practices on construction sites to improve health and safety practices.

First, this study reviewed previous studies to derive the factors affecting health and safety practices on construction sites, and based on the literature review, the 50 factors identified were used in developing the questionnaire that was administered to construction professionals. The returned questionnaire was then analysed using fuzzy synthetic evaluation (FSE).

According to the FSE results, inadequate safety planning has the highest critical value, followed by management failure, inadequate safety promotion, safety ignorance, poor supervision and enforcement, a casual attitude towards safety and insufficient experience. The analysis showed that the overall criticality of the factors to the Malaysian construction industry is high, suggesting that if addressed, the health and safety practices of the industry will be improved.

The study provides a holistic approach to the factors affecting health and safety practices in the construction industry using a method that has not been used by previous researchers. The findings will furnish decision makers in the construction industry with practical approaches to adapt and reinforce in the industry to improve health and safety practices.

In the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.

Authors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.

Combining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.

This paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.

This article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.

This study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.

Coastal cities offer great ecological, cultural and economic benefits due to their tourism potential. The objective of this research is to (1) identify tourists' post-pandemic motivations, (2) establish a post-pandemic demand segmentation and (3) determine the relationship between post-pandemic segments and loyalty.

This study was carried out in Lima, Peru, a tourist destination on the Pacific Ocean coast. The sample was collected between June and July 2020, during the coronavirus disease 2019 (COVID-19) pandemic. In total, 354 valid questionnaires represented the sample size of this quantitative study. For data analysis, factor analysis and K-means non-hierarchical clustering were used.

The results show four post-pandemic motivational dimensions in coastal cities: “novelty and escape,” “learning and culture,” “destination safety” and “service safety.” Likewise, there are two post-pandemic segments in coastal cities: “safety seekers” who want to feel safe at the destination and with its services, and “multiple motives,” motivated by several reasons simultaneously, such as safety, novelty and escape, and learning and culture. The multiple motives group shows higher return intentions, making it a crucial post-pandemic segment in coastal cities.

The limitations of the present study were the online sampling and the timing when collecting the data since the demand can vary due to seasonal reasons.

Since coastal cities have natural and cultural attractions appealing to many travelers, they should adopt the necessary biosecurity measures to attract the safety seekers’ segment, who wants to feel safe at the destination and with its services. Similarly, the multiple motives’ segment favors safety over other recreational activities in the coastal area, so it is necessary that activities such as sports on the beach, walks, observation of flora and fauna, navigation and interaction with the community, meet the required biosecurity standards.

The results will be used to plan the following actions in coastal destinations and meet the tourists’ demands when this health crisis ends.

In this context, up to date, demand segmentation by motivations in coastal cities during the COVID-19 pandemic has not been investigated. Such a study will help to obtain post-pandemic results regarding the tourism demand for these destinations. To date, there are no studies in coastal cities that analyze demand segmentation and its motivations for the post-COVID-2019 pandemic.

Given the cruciality of construction workers' safe behaviors, the possible influential factors on workers' behaviors should be studied, and one of these factors is characteristics. The authors identified emotional intelligence (EI), motivation and job burnout as characteristics that might affect a worker's safety behavior, and the aim of this study is to investigate these possible relationships.

Workers' EI, motivation and job burnout status were assessed by a structured interview. Furthermore, workers' safety behaviors were assessed by a checklist derived from national codes, regulations and other research studies. Then, the researcher's observations took place, and the data were acquired.

EI and motivation of workers were able to predict safety behaviors, and the effect of job burnout on safety behaviors was not significant. In addition, motivation's influence on job burnout was not significant. Therefore, in order to promote safety behaviors, the EI and motivation of workers need to be taken into consideration.

The results indicate why construction managers should consider the workers' EI and motivation competencies and how this consideration could lead to safer and better performance in construction projects.

The possible effects of EI, motivation and job burnout on the safety behaviors of construction workers haven't been paid enough attention. Moreover, the authors couldn't find a study similar to the present one that was conducted in Iran. Also, an original model was presented, and safety behaviors were studied through fieldwork rather than using questionnaires.

Deployment of health and safety standards in extremely hazardous work environments such as oil and gas sector, is essential to minimise accidents leaving employees permanently or temporarily incapacitated. The purpose of this research, is to understand why there are frequent accidents in case country's oil and gas sector, with a view to recommend solutions to mitigate problems.

Research methodology involved extensive review of the literature to appreciate background, and current research on typical accidents and safety measures taken at oil and gas construction sites to minimise accidents in a middle east country. Interviews, questionnaires and case stidies were deployed to acquire data which highlighted major reasons for accidents occurrence at oil and gas construction sites, and safety tools and techniques that could reduce accident rate if adopted by companies.

Findings, showed that oil and gas construction projects in case country were prone to health and safety related risks, challenges and accidents due to failure to comply with standards and legislation. Construction site teams and shop floor staff were rarely involved in development of safety policies, and some had no understanding of requirements and procedures underpinning safety during operations. Research recommended rapid application/adoption of international standards underpinned by ISO 45000 series and staff training at all levels. Deployment of robots and use of machine learning technology were suggested to implement risky tasks in the sector.

Research was based on rampant accidents occurring in hazardous oil and gas sector in country studied. Enforcement of health and safety standards, and use of modern tools and techniques were recommended to minimise accident rate.

With increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.

This study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.

The research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.

This study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.

Given the growing concern about employees' well-being, numerous researchers have investigated the causes and effects of occupational stress. However, a review study on identifying existing research topics and gaps is still deficient in the extant literature. To fill this gap, this review study aims to present a bibliometric and science mapping approach to review the state-of-the-art journal articles published on occupational stress in the construction industry.

A three-fold comprehensive review approach consisting of bibliometric review, scientometric analysis and in-depth qualitative discussion was employed to review 80 journal articles in Scopus.

Through qualitative discussions, mainstream research topics were summarized, research gaps were identified and future research directions were proposed as follows: versatile stressors and stress model; an extended subgroup of factors in safety behavior; adaptation of multiple biosensors and bio-feedbacks; evaluation and comparison of organizational stress interventions; and incorporation of artificial intelligence and smart technologies into occupational stress management in construction.

The findings of this review study present a well-rounded framework to identify the research gaps in this field to advance research in the academic community and enhance employees' well-being in construction.