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Hazard identification is a fundamental step in safety management that has the potential to reduce the number and severity of occupational injuries on construction sites. Researchers have identified and evaluated some of the hazards, but few have extensively discussed all of them and none have classified them by sector. The goal of this paper is to fill that research gap by considering hazard identification through an organized synthesis of the existing literature. After a comprehensive literature review, 236 publications were deemed eligible for further analysis. Eighteen safety hazards were identified and then categorized into four groups based on their physiological impacts, ranked based on frequency of citation, and classified by sector. The results revealed that falls from heights, material handling, and heavy machinery were the most frequently cited hazards and the most likely to impact all sectors. Mitigation strategies were also identified, and it was determined that most hazards can be mitigated through the use of personal protective equipment, and effective training and supervision. [ABSTRACT FROM AUTHOR]

The construction process is a dynamic one, and the complexity of the working conditions and the high level of uncertainty make the construction industry the third most dangerous industry after mining and agriculture. And since the construction industry is vital to the development of a country, safety during construction is of particular importance. A great deal of research, studies and practices have been conducted to reduce potential risks and improve worker efficiency during the construction process. In recent years, with the rapid development of cognitive neuroscience and the integration of medical technology, various wearable monitoring devices have been widely used in the field of building construction for real-time monitoring of workers' physical and mental conditions. Among them, the application of EEG (electroencephalogram) in the building construction process enables researchers to gain insight into the physical and mental state of construction workers while performing construction tasks. This paper introduces EEG technology and portable EEG monitoring equipment and summarizes its application in monitoring workers' adverse reactions (emotion, fatigue, psychological burden, and vigilance) and construction hazard identification during the process of construction in recent years, which provides future EEG research in the field of building construction and construction site safety management. [ABSTRACT FROM AUTHOR]

For mining engineering and geological engineering, the deformation and fracture of coal and rock are the essential causes of dynamic disasters. In this paper, the loading experimental system was established on gas-bearing coal, to study the internal relationship between the spatial distribution of EP and the development process of damaging localization on coal mass. The results indicated the temporal response of EP signal could reflect the stress state and damage degree of gas-bearing coal, showing the precursor feather when coal mass failed. Particularly, when the stress dropped suddenly, the EP signal has a sudden mutation, and the acoustic emission (AE) signal suffered the pulsed increase, while the crack grew and expanded rapidly. Furthermore, when local severe damage occurs inside the coal mass, there was abnormal EP spatial distribution with high-value or dense contours in these zones, where simultaneously AE rupture events frequently occurred, and cracks expanded and gathered. The characteristics of EP spatial distribution can reveal the development process of coal mass damaging localization. Meanwhile, the mentioned zones with abnormal EP distribution above are hazard sources inducing coal mass’s instability and failure. The study results provide the foundational research work of fine forecasting on coal and gas outburst disaster hazard. [ABSTRACT FROM AUTHOR]

Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens. [Display omitted] • Types of endocrine disrupting chemicals (EDCs) related to cancer were analyzed. • EDCs associated cancers and their mechanisms were discussed. • Human exposure to EDCs was explored. • Assessment of EDCs carcinogenicity was performed. • Research gaps and future studies were suggested. [ABSTRACT FROM AUTHOR]

Sucralose, a sugar substitute first approved for use in 1991, is a non-caloric sweetener regulated globally as a food additive. Based on numerous experimental animal studies (dating to the 1980s) and human epidemiology studies, international health agencies have determined that sucralose is safe when consumed as intended. A single lifetime rodent carcinogenicity bioassay conducted by the Ramazzini Institute (RI) reported that mice fed diets containing sucralose develop hematopoietic neoplasia, but controversy continues regarding the validity and relevance of these data for predicting health effects in humans. The present paper addresses the controversy by providing the perspective of experienced pathologists on sucralose-related animal toxicity and carcinogenicity data generally, and the RI carcinogenicity bioassay findings specifically, using results from publicly available papers and international regulatory authority decisions. In the authors' view, flaws in the design, methodology, data evaluation, and reporting of the RI carcinogenicity bioassay for sucralose diminish the value of the data as evidence that this agent represents a carcinogenic hazard to humans. This limitation will remain until the RI bioassay is repeated under Good Laboratory Practices and the design, data, and accuracy of the pathology diagnoses and interpretations are reviewed by qualified pathologists with experience in evaluating potential chemically-induced carcinogenic hazards. [ABSTRACT FROM AUTHOR]

Crane-related accidents often occur due to failure in timely and accurate hazard identification. Virtual Reality (VR) technology facilitates immersive environments for improving hazard identification without real-world consequences. However, the impact of simulation fidelity on hazard identification performance, particularly for novice crane operators, remains underexplored. This paper examines how simulation fidelity and task complexity affect hazard identification performance among novice operators through a VR experiment with two fidelity and two complexity levels. Findings demonstrate high-fidelity (HF) scenarios significantly improve hazard identification success rate and speed for swing-over hazards but extend task completion times. Task complexity modulates simulation fidelity's impact on task completion, with significant differences between fidelity levels observed only under low complexity. These insights inform the design and calibration of simulation fidelity in VR platforms, potentially improving training efficacy and crane lifting safety for novice operators. • Explores the impact of simulation fidelity on novice crane operators' hazard identification. • Finds that high-fidelity boosts success rate but reduces speed in hazard identification. • Highlights accuracy-speed trade-off due to simulation fidelity changes. • Emphasises interaction fidelity for immersive, real-world-like experiences. • Guides on balancing simulation fidelity and cost-effectiveness in VR safety training. [ABSTRACT FROM AUTHOR]

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system's response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks. [ABSTRACT FROM AUTHOR]

Given the dynamic and complex nature of a construction jobsite, workers' safety is significantly connected to both their ability to efficiently scan their surrounding environment and their ability to anticipate and identify potentially hazardous sources in a timely manner. However, existing studies have failed to consider the spatial-temporal characteristics of high-risk construction hazards as well as the limited training available for enhancing workers' hazard anticipation. Accordingly, this study (1) examines the impact of dynamic hazards' spatiotemporal characteristics on construction workers' hazard identification and anticipation performance, and (2) proposes and illustrates a new methodological approach that harnesses cue perception timing to assemble a coherent cognitive model of situational awareness. To achieve these objectives, this study combines subjective survey measurements with such technologies as 360° video panoramas and wearable psychophysiological sensors to examine 30 construction workers' hazard search strategies, timely hazard identification performance, and spatial attentional distribution. The results of this study demonstrate that workers' hazard anticipation abilities are differentially influenced by both spatial and temporal characteristics of dynamic hazards: (1) most workers failed to distribute their spatial attention across the 360° construction environment and missed hazards outside their field of view (behind or overhead), highlighting the importance of attentional orienting; (2) most workers failed to identify emerging hazard cues that would enable anticipating latent hazards; and (3) many workers failed to develop a correct situational awareness framework because they had improper cue-perception timing. The findings of this study highlight the importance of considering the unique spatiotemporal characteristics of dynamic hazards to properly predict workers' true hazard anticipation abilities. Furthermore, the findings of this study are expected to provide valuable insight into developing effective safety training strategies to enhance workers' situational awareness. [ABSTRACT FROM AUTHOR]

The cascading propagation and evolution of metro operation failures can significantly impact the safety of metro operation. To overcome this challenge, this study pre-processes a massive amount of metro operation log data through noise reduction. Moreover, a professional terminology dictionary is constructed along with a custom stop-word dictionary to segment the preprocessed data. Subsequently, the AFP-tree algorithm is employed to mine the segmented log data and identify key hazards. A weighted urban rail transit network is established, considering the effective path time cost, and the shortest travel OD path. To simulate the dynamic evolution of the failure chain propagation, a model based on disaster propagation theory is constructed. Taking the Shanghai Metro line as a case, multiple simulation scenarios are established with 25 key hazards as triggering points, and the number of cascade failure stations affected under different scenarios is outputted. The results indicate that the fault stations caused by the large passenger flow are the largest. Meanwhile, the number of stations affected by the door clamp is the smallest. The scale of fault stations reaches a maximum value in 16–20 min. Through case analysis, a positive correlation is found when the self-recovery factor is between 14 and 18, and the number of fault stations shows a significant increasing trend. The research results can provide decision-making support and theoretical guidance for rail transit operation safety management enterprises. [ABSTRACT FROM AUTHOR]

Hazard identification is a fundamental step in safety management that has the potential to reduce the number and severity of occupational injuries on construction sites. Researchers have identified and evaluated some of the hazards, but few have extensively discussed all of them and none have classified them by sector. The goal of this paper is to fill that research gap by considering hazard identification through an organized synthesis of the existing literature. After a comprehensive literature review, 236 publications were deemed eligible for further analysis. Eighteen safety hazards were identified and then categorized into four groups based on their physiological impacts, ranked based on frequency of citation, and classified by sector. The results revealed that falls from heights, material handling, and heavy machinery were the most frequently cited hazards and the most likely to impact all sectors. Mitigation strategies were also identified, and it was determined that most hazards can be mitigated through the use of personal protective equipment, and effective training and supervision.

The construction process is a dynamic one, and the complexity of the working conditions and the high level of uncertainty make the construction industry the third most dangerous industry after mining and agriculture. And since the construction industry is vital to the development of a country, safety during construction is of particular importance. A great deal of research, studies and practices have been conducted to reduce potential risks and improve worker efficiency during the construction process. In recent years, with the rapid development of cognitive neuroscience and the integration of medical technology, various wearable monitoring devices have been widely used in the field of building construction for real-time monitoring of workers’ physical and mental conditions. Among them, the application of EEG (electroencephalogram) in the building construction process enables researchers to gain insight into the physical and mental state of construction workers while performing construction tasks. This paper introduces EEG technology and portable EEG monitoring equipment and summarizes its application in monitoring workers’ adverse reactions (emotion, fatigue, psychological burden, and vigilance) and construction hazard identification during the process of construction in recent years, which provides future EEG research in the field of building construction and construction site safety management.