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Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These incidents are often attributed to inadequate supervision on construction sites and workers' low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers' personal protective equipment and monitoring and recognizing when workers enter hazardous areas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in detection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture. [ABSTRACT FROM AUTHOR]

In the dynamic construction environment, workers' safety heavily depends on their ability to effectively perceive and react to hazards. Accordingly, studies have assessed the status of workers' risk perception using advanced technologies. However, these studies have mainly focused on whether risks are perceived rather than how they are perceived. Recognizing the need for effective safety interventions that address risk-perception failures, it becomes crucial to not only classify workers' risk-perception states but also to delve into the underlying processes of their risk perception. To address this research gap, this study examines the critical aspect of risk perception in construction safety by employing functional near-infrared spectroscopy (fNIRS) and 360° panoramas from actual construction sites to assess workers' cognitive processes during hazard identification. Classifiers were developed using the AutoML method, and 15 advanced machine learning algorithms were compared to identify the highest-performing model. This model would then be utilized to understand the risk-perception process by incorporating the feature-importance technique. The results indicate that CatBoost emerged as the most effective classifier, achieving an accuracy rate of 90.3%. Additionally, the results identify significant brain activations in four anatomical locations: the prefrontal cortex, frontal eye fields, primary motor cortex, and primary auditory cortex. Notably, there is a significant correlation between these areas, emphasizing the importance of both visual and auditory cue perception in shaping workers' situational awareness. This research highlights the potential of neuroimaging fNIRS in improving construction safety, and the importance of auditory perception in hazard identification, offering insights that could enhance the effectiveness of safety training programs. [ABSTRACT FROM AUTHOR]

There is emerging evidence that negative emotions can be leveraged to help construction workers recognize hazards, with fear being closely associated with avoidance behavior among negative emotions. Previous research has indicated that decision-making during a simulated crisis differs significantly from real crisis scenarios. In order to induce fear arousal based on actual harm (referred to as real fear arousal), this study utilized the high degree of consistency between humans and mice in conducting a three-phase experiment employing a modified two-way active avoidance paradigm. The study's key finding indicates that integrating construction elements into the modified two-way active avoidance paradigm can activate the renewal of active avoidance behavior toward shocks under conditions of fear arousal throughout the experiment. The level of fear demonstrated a significant impact on active avoidance of hazards (AAH) in the three-phase experiment. Practical Applications: Managerial decision-making under crisis theory emphasizes the notable disparity between decision-making in simulated crisis scenarios and actual crisis situations. Consequently, effectively intervening in construction workers' risk-taking behaviors often proves challenging. Numerous studies have established a close relationship between fear and avoidance behaviors. In order to ethically and effectively explore the influence of fearful emotions on real-life risk-taking behaviors, a three-phase avoidance experiment (comprising acquisition, extinction, and renewal phases) was conducted based on the classical paradigm used in experimental methods of animal behavior. Therefore, appropriately inducing fear arousal may enhance construction workers' ability to avoid risk, particularly among those who have previously been injured, with potentially more pronounced effects. We advocate for a focus on enhancing the fear experience within reasonable limits during future safety training initiatives. [ABSTRACT FROM AUTHOR]

Currently, the incidence of accidents involving falls from height at construction sites caused by workers climbing guardrails is still high. Traditional unsafe behavior management mainly relies on a safety patrol of construction-site supervisors, which consumes considerable laborpower and time. There is still a critical need for an automated safety management method to identify unsafe guardrail climbing behavior. This study proposes a worker behavior identification method based on visual data fusion of a worker's surrounding environment and posture data. Videos of seven participants' guardrail climbing behavior through multiangle and multidistance cameras were analyzed to verify this method. By analyzing the environment and posture of the participants, three methods based on environment, posture, and fusion data were used to detect the stage of guardrail climbing action of the workers and compare them with the ground truth labeled by safety experts. The precision and recall of worker guardrail climbing behavior based on the fusion method were 82% and 83% respectively, which is better performance than that obtained using a single method. The data fusion–based method avoids the misjudgment generated by a single detection method and can identify the guardrail climbing behavior more accurately. Practical Applications: Guardrail climbing is a typical unsafe behavior that exposes workers to a high risk of falling from height. However, there is a lack of research on the interaction between workers and guardrail systems in the construction industry. This study provides a nonintrusive method for automating detection and management of guardrail climbing behavior on construction site. Using existing surveillance cameras, this method can be deployed at low cost with slight interference with workers. Based on the detection, appropriate interventions are expected to effectively reduce workers' unsafe behaviors during construction and improve safety on site. The detection of guardrail climbing, which is one of the variety of unsafe behaviors associated with falls from height, can enrich the intelligent construction safety management system effectively. Moreover, this study also provides reference and quantitative indicators (e.g., a guardrail climbing unsafe behavior database) for risk assessment and early warning of workers who are exposed to risk of fall from height. [ABSTRACT FROM AUTHOR]

In the construction of prefabricated buildings, safety issues occur frequently, posing challenges to project progress and personnel safety. As a new trend in the construction industry, the complexity of the environment in prefabricated construction demands an update to traditional safety management concepts. This study introduces the concept of resilience to analyze safety issues in prefabricated construction and develops a WSR-4Rs framework for a systematic evaluation of construction safety. The study first combines the WSR (Wuli-Shili-Renli) systematic methodology with the 4R resilience theory to construct an evaluation index system for construction safety. Then, it uses the Analytic Hierarchy Process (AHP) and the entropy weight method to determine the combined weights of each index, establishing a balanced and objective weighting scheme. A fuzzy comprehensive evaluation model is then applied to assess actual project cases. Finally, an obstacle degree model is introduced to identify key indicator factors that significantly impact construction safety, and specific improvement measures are proposed based on these findings. The aim is to provide practical references and guidance for enhancing the safety management level in prefabricated construction. [ABSTRACT FROM AUTHOR]

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice. [ABSTRACT FROM AUTHOR]

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project's safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms. [ABSTRACT FROM AUTHOR]

Traditional teaching methods like lectures can hinder the integration of theoretical knowledge and practical skills in higher education. To address this challenge, digital simulation games (DSGs) offer promising solutions through immersive and interactive learning experiences. Research shows that DSGs can motivate learners, enhance subject interest, and improve practical skill development in higher education. Authentic learning, which incorporates real-world contexts, tasks, and assessments, can address this gap by enhancing engagement and critical thinking. Unfortunately, there are no validated instruments to measure the effectiveness of DSGs and authentic learning. This study aimed to develop and validate the authentic digital simulation game (ADSG) questionnaire to assess DSGs' effectiveness in higher education. The ADSG questionnaire was administered to 155 undergraduates who utilized a construction hazard identification DSG for a construction safety course. Statistical analyses were conducted, including exploratory and confirmatory factor analyses (EFA and CFA), logistic regression, and internal consistency reliability assessments. The 17-item scale generated four significant factors: (1) collaboration and sharing of ideas, (2) authenticity of context, (3) clear objectives and guidance, and (4) game design elements. The CFA confirmed the revised model's validity (CFI = 0.92, RMSEA = 0.07) and the logistic regression model was statistically significant (χ2 (4, N = 155) = 28.860). The odds ratios are 0.33, 1.71, 2.28 and 0.83 respectively. Clear objectives and guidance were found to have the most significant impact on the perceived effectiveness of DSGs, while game design elements had less influence. This study provides a valuable tool for educators and practitioners to evaluate and enhance DSGs effectively in higher education. [ABSTRACT FROM AUTHOR]

Work-related musculoskeletal disorders (WMSDs) represent a significant health challenge for workers in construction environments, often arising from prolonged exposure to ergonomic risks associated with manual labor, awkward postures, and repetitive motions. These conditions not only lead to diminished worker productivity but also incur substantial economic costs for employers and healthcare systems alike. Thus, there is an urgent need for effective tools to assess and mitigate these ergonomic risks. This study proposes a novel monocular 3D multi-person pose estimation method designed to enhance ergonomic risk assessments in construction environments. Leveraging advanced computer vision and deep learning techniques, this approach accurately captures and analyzes the spatial dynamics of workers' postures, with a focus on detecting extreme knee flexion, a critical indicator of work-related musculoskeletal disorders (WMSDs). A pilot study conducted on an actual construction site demonstrated the method's feasibility and effectiveness, achieving an accurate detection rate for extreme flexion incidents that closely aligned with supervisory observations and worker self-reports. The proposed monocular approach enables universal applicability and enhances ergonomic analysis through 3D pose estimation and group pose recognition for timely interventions. Future efforts will focus on improving robustness and integration with health monitoring to reduce WMSDs and promote worker health. [ABSTRACT FROM AUTHOR]

To address the accuracy limitations of existing safety helmet detection algorithms in complex environments, we propose an enhanced YOLOv8 algorithm, called YOLOv8- CSS. We introduce a Coordinate Attention (CA) mechanism in the backbone network to improve focus on safety helmet regions in complex backgrounds, suppress irrelevant feature interference, and enhance detection accuracy. We also incorporate the SEAM module to improve the detection and recognition of occluded objects, increasing robustness and accuracy. Additionally, we design a fine-neck structure to fuse features of different sizes from the backbone network, reducing model complexity while maintaining detection accuracy. Finally, we adopt the Wise-IoU loss function to optimize the training process, further enhancing detection accuracy. Experimental results show that YOLOv8-CSS significantly improves detection performance in general scenarios, complex backgrounds, and for distant small objects. YOLOv8-CSS improves precision, recall, mAP@0.5, and mAP@0.5:0.95 by 1.67%, 5.55%, 3.38%, and 5.87%, respectively, compared to YOLOv8n. Our algorithm also reduces model parameters by 21.25% and computational load by 15.89%. Comparisons with other mainstream object detection algorithms validate our approach's effectiveness and superiority. [ABSTRACT FROM AUTHOR]

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]

The establishment of Indonesia’s new capital, Ibu Kota Nusantara, was a massive project that created significant risks during the construction phase, such as construction accidents. In response, total construction safety culture was developed to make a belief and implement strategies for minimizing risks. This research aimed to recommend strategies based on a structural equation model of total construction safety culture to improve safety performance. Using structural equation modeling with a partial least square approach, strategies were categorized into two aspects, covering the macro impact of construction accidents (national scope) and the micro and meso impacts (company and project scope). The macro strategy recommended the creation concept of nomenclature and criteria within government regulations related to construction safety The suggestion for the government regualation would cover construction safety ecosystem in Indonesia. Meanwhile, the micro and meso strategies concept included practical steps such as technology transformation, tacit knowledge, and improved supervision methods. By transformationing technology in construction safety such as using movement sensor and Building Information Modeling, it will be helpfull for the contractors to monitor all of the manpower and create safer working enivronment. Additionally, they can be applied in other cases to minimize the risk of construction accidents. [ABSTRACT FROM AUTHOR]

The architecture, engineering, and construction (AEC) industry faces ongoing challenges in enhancing safety performance. Despite the availability of advanced technologies for enhancing safety, there is limited understanding of the inter-relationships among safety factors and advanced technologies for enhancing safety performance. This study aims to investigate the inter-relationships among factors affecting safety performance and advanced technologies. A questionnaire survey was disseminated to construction professionals to assess the criticality of factors and strategies. The data were analyzed using descriptive statistics, correlation analysis, and exploratory factor analysis (EFA). The findings indicate that 16 factors and eight advanced technologies are critical for enhancing safety. The EFA grouped 11 critical factors into four underlying groupings: safety planning and hazard prevention, workplace environment and supervision, employee safety support, and medical readiness and site protection. Moreover, the EFA grouped the eight critical advanced technologies into two underlying groupings: advanced digital technologies and personal and site monitoring technologies. The correlation analysis demonstrates measurable but weak associations between the factors and advanced technologies, indicating the need for future research to explore additional variables that may impact these relationships. The findings help construction professionals prioritize resources to address the specific groupings of critical factors and advanced technologies. [ABSTRACT FROM AUTHOR]

Personal protective equipment (PPE), including helmets, harnesses, and lanyards, is pivotal in preventing falls from heights at construction sites. However, ensuring consistent and correct usage of PPE presents a significant challenge. To address this issue, this study introduces an enhanced You Only Look Once, version 8 model (YOLOv8), a computer-vision-based AI model tailored for real-time multiclass PPE monitoring on portable edge devices. A pioneering large-scale multiclass PPE data set is curated to facilitate model training. Balancing detection accuracy with a lightweight design, we augment YOLOv8 through the integration of the coordinate attention module, ghost convolution module, transfer learning, and merge-nonmaximum suppression. The proposed model surpasses the original YOLOv8 and state-of-the-art models, showcasing improved accuracy and reduced computational cost. Deployed on the edge device Jetson Xavier NX, the model achieves precise PPE detection (mAP50 : 92.52%) in real-time, operating at 9.11 frames per second. These findings establish a robust foundation for the efficient and real-time automated safety monitoring of construction sites, promising substantial enhancements to worker safety and data privacy within the construction industry. [ABSTRACT FROM AUTHOR]

Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improving the timeliness and security of image recognition. This study is indented to investigate the potential costs and benefits of AIoT-IS applications. This study summarizes AIoT-IS application scenarios for construction safety monitoring and proposes a cost/benefit analysis method for AIoT-IS implementation projects. According to the case study results, AIoT-IS achieves significant benefits, with a Net Present Value Index (NPVI) of 19.17% and a Benefit/Cost Ratio (BCR) of 4.65 as applied to construction site safety monitoring. Interviews with domain experts also provided qualitative feedback, pointing to the directions for future research. The proposed method is applicable for the decision-making of AIoT-IS adoption and the feasibility assessment of other innovative construction technologies.

Abstract Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

AbstractThe rail construction industry is notable for its large scale, substantial investment, extensive stakeholders involvement, long construction period, and intricate operation and technology. This industry is among the most dangerous due to the highest number of occupational accident cases worldwide. Therefore, it is crucial to analyse and identify the existing literature on occupational accident factors in rail construction. To address the research aim, the study identified the factors that contribute to occupational accidents using systematic review methodology. This systematic literature review adheres to the rigorous Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Relevant publications from the past 25 years were retrieved from Scopus, Web of Science (WoS), and Science Direct electronic databases. Through a meticulous review of 43 selected publications, five accident factor themes were discovered: worker, workplace, materials and equipment, organizational, and environmental influences. The detailed analysis of these themes has led to the identification of 19 specific sub-factors within these categories, providing a granular understanding of the intricate elements contributing to accidents. This study offers a foundational understanding of accident factors in the rail construction industry, paving the way for targeted OSH interventions aimed at preventing occupational accidents in the future. [ABSTRACT FROM AUTHOR]

The enhancement of construction safety performance heavily relies on effective safety training. While virtual reality (VR) technologies have been utilized to improve construction safety training programs, the extent and mechanisms of improvement brought by VR remain unexplored. This study provided explanations on how the effectiveness of VR-based safety training for a heterogeneous workforce was achieved by investigating two mechanisms, namely embodied cognition and emotion arousal, from the physiological perspective. Randomized controlled experiments were conducted with three forms of safety training, namely paper-based training, VR-based learning, and VR-based experiencing, for both novice learners (NPs) and learners with prior knowledge (PPs). Digital eye-tracking and physiological devices and measurements were used to collect objective data. The results revealed better hazard recognition performance in both VR-based learning and VR-based experiencing groups than that in paper-based training groups. The results also revealed that VR-based learning was more effective for NPs than for PPs in acquiring safety knowledge, but VR-based experiencing was more effective for PPs than for NPs in stimulation of emotions. This means that the NPs benefit more from embodied cognition provided by the immersive environment of VR-based learning, and the PPs would be trained better with emotional arousal from the thrill of VR-based experiencing. The discovered mechanisms of embodied cognition and emotion arousal shed light on the underlying processes that contribute to the positive outcomes and promotion of VR-based training. [ABSTRACT FROM AUTHOR]

Construction equipment operators (CEOs), who are required to work in seated positions for prolonged periods, often develop excessive mental fatigue, causing human error-related accidents, lower productivity, and psychological illnesses. However, the current practice for assessing fatigue is limited on construction sites. Previous studies utilizing smartwatches, electroencephalography, or eye-tracking technologies are intrusive and not convenient since they require operators to wear special devices, while vision-based solutions are sensitive to lighting conditions and have serious privacy concerns. There is a demand for continuously and accurately monitoring CEOs' mental fatigue levels without causing discomfort and aversion. This study introduces a noninvasive and noncontact smart cushion method to bridge the knowledge gap. We first developed a smart cushion system incorporating optical fiber sensors to collect human heartbeat and respiration data. Then, we adopted the Bidirectional Long-Short-Term Memory (BiLSTM) model to recognize fatigue states. An experiment was conducted in which data was collected from 16 subjects engaged in simulated excavation tasks. Experimental results demonstrate the feasibility of the proposed method, and the BiLSTM model obtained an accuracy of 94.0%. The proposed smart cushion method could also be convenient for understanding ergonomic risks resulting from prolonged sitting, a grave occupational health and safety problem that plagues various industries. Practical Applications: This study presents a smart cushion–based framework to continuously monitor the mental fatigue states of construction equipment operators (CEOs) during daily work. The proposed solution has clear advantages: it (1) is nonintrusive since it no longer requires sensors attached to the skin of operators, (2) is not sensitive to dynamic lighting conditions and does not generate privacy concerns, (3) is easy-to-use since it can be placed on the operator's seat or seatback and does not need additional power. Experimental results also demonstrated that the proposed Gaussian mixture model and Bidirectional Long-Short-Term Memory model can achieve effective data processing and accurate fatigue recognition. The proposed system can provide construction managers with a quantitative and reliable assessment tool to measure CEOs' mental workloads and support the intervention (e.g., worker shifts and breaks). If wirelessly connected to a smartphone, the smart cushion can conveniently provide early warnings to operators and their managers to alert the operators at stake to take breaks/rests to avoid mental fatigue-related ill consequences. Moreover, the proposed solution could be used to study and prevent the risks resulting from prolonged sitting, which is a grave occupational health and safety problem plaguing many industries. [ABSTRACT FROM AUTHOR]

Due to the dynamic nature of work environments and conditions in construction, it is necessary to perform a job hazard analysis (JHA) prior to the commencement of hazardous jobs, and regularly review and update it. JHA is considered an intellectual activity subject to substantial influence by the experience and knowledge of the individuals conducting the analysis. Given the manual nature of JHA in current practice, its thorough preparation and use are time-consuming and laborious; thus, there is a great need to automate it. Against this background, this research aimed to develop a conceptual ontological model that can support the automation of JHA processes, including the tacit knowledge possessed by experts to facilitate automation. A JHA document analysis and a qualitative Delphi study were adopted to identify the concepts and associations embedded in JHA. An abductive data analysis approach was used with the guidance of a theoretical understanding of the systems model of construction accident causation to analyze the data collected from JHA documents and interviews. The findings offer valuable insights into important entities, subentities, and relationships that are associated with hazard identification and risk assessment, which form the basis for developing a conceptual ontological model. Such an ontology can facilitate the automation of JHA with an enhanced level of reasoning capability, through which the efficiency and effectiveness of JHA on construction sites can be improved. [ABSTRACT FROM AUTHOR]

Falls are the most common type of accident in the construction industry, and falls to a lower level are among the leading causes of fatalities. Work-related fatalities due to falls, slips, and trips have been increasing, with Hispanic workers among the highest fatalities. This study investigated the association between fall accidents and attributes such as age, musculoskeletal pain (MSPs), sleep hours, safety knowledge, use of personal protective equipment (PPE), and working hours among Hispanic construction workers involved in building construction. This study collected 220 valid responses and used nonparametric chi-square tests and binary logistic regression to analyze the data. This study found that the location of the fall, MSPs, and use of personal protective equipment have a significant effect on the likelihood of having fall accidents. The strongest predictor of fall accidents was "fall from a ladder", followed by having two or three MSPs. The use of PPE had the highest decreasing ratio in odds of fall accidents, indicating the importance of wearing PPE properly. The results show the importance of integrating safety management strategies within construction projects' broader life cycle management. The insights list how project engineers can incorporate these findings into Building Information Modeling (BIM) systems to enhance project planning and safety measures in reducing fall-related accidents and their severe consequences. This study highlights the importance of addressing MSPs, properly using PPE, and reducing falls from ladders in the construction industry to prevent fall accidents among Hispanic workers and minimize their severe consequences. [ABSTRACT FROM AUTHOR]

Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites. [ABSTRACT FROM AUTHOR]

To address the common issues of lacking indicator system identification, causal relationship quantification, and path simulation analysis in the current research on safety performance in prefabricated construction, a method for improving safety performance in prefabricated construction based on the decision-making trial and evaluation laboratory (DEMATEL) and NK model is proposed. Firstly, through theoretical analysis and literature review, the indicator system for safety performance in prefabricated construction is identified using the grounded theory. Secondly, expert research and quantitative analysis are combined to analyze the causal relationship of the indicators using the DEMATEL method. Then, the DEMATEL method is integrated with the NK model to carry out a key indicator adaptability modeling analysis and three-dimensional simulation. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and method. The results show that X6 (construction and implementation of safety management system) had the highest impact on the other indicators, and X14 (quality and safety status of prefabricated components) was most influenced by other indicators. X6 (construction and implementation of safety management system), X1 (personnel safety awareness and attitude), X14 (quality and safety status of prefabricated components), and X12 (construction site working environment) were identified as key performance indicators. "X6 (construction and implementation of safety management system) → X1 (personnel safety awareness and attitude) → X14 (quality and safety status of prefabricated components) → X12 (construction site working environment)" was considered the optimal path to improve construction safety performance. [ABSTRACT FROM AUTHOR]

Due to the complex and dynamic nature of construction environments, safety accidents occurring in these environments pose a grave threat to life and property. Therefore, it is essential for safety managers in construction, supervisory, and related units to adopt a rigorous and systematic methodology for assessing the risks associated with construction safety accidents. This will enable managers to comprehend the likelihood of accidents, subsequently enabling them to implement preemptive and control measures to reduce the probability of such incidents. Drawing on the accident causation theory, this study utilized web crawler technology to collect construction accident reports, subsequently employing text mining (TM) techniques to identify the accident causal factors specified in 166 accident reports. Subsequently, 33 key features were extracted from the accident causal factors, and correlation rule mining was used to analyze the correlations between the causal factors. Successively, a Bayesian network (BN)-based risk assessment model was constructed for construction safety accidents. Finally, through reverse reasoning, this study identified the probable paths of construction safety accidents and the sensitive factors that trigger such accidents. The results showed that management factors (MFs) are the primary drivers of accidents, highlighting the importance of focusing on preventive and control countermeasures for factors characterized with high severity and sensitivity. [ABSTRACT FROM AUTHOR]

Excavating super-large-span tunnels in soft rock masses presents significant challenges. To ensure safety, the sequential excavation method is commonly adopted. It utilizes internal temporary supports to spatially partition the tunnel face and divide the excavation into multiple stages. However, these internal supports generally impose spatial constraints, limiting the use of large-scale excavation equipment and reducing construction efficiency. To address this constraint, this study adopts the "Shed-frame" principle to explore the feasibility of an innovative support system, which aims to replace internal supports with prestressed anchor cables and thus provide a more spacious working space with fewer internal obstructions. To evaluate its effectiveness, a field case involving the excavation of a 24-m span tunnel in soft rock is presented, and an analysis of extensive field data is conducted to study the deformation characteristics of the surrounding rock and the mechanical behavior of the support system. The results revealed that prestressed anchor cables integrated the initial support with the shed, creating an effective "shed-frame" system, which successively maintained tunnel deformation and frame stress levels within safe regulatory bounds. Moreover, the prestressed anchor cables bolstered the surrounding rock effectively and reduced the excavation-induced disturbance zone significantly. In summary, the proposed support system balances construction efficiency and safety. These field experiences may offer valuable insights into the popularization and further development of prestressed anchor cable support systems. [ABSTRACT FROM AUTHOR]

The construction industry presents significant high risks of injury and fatality to its workforce. Adopting prevention through design (PtD) principles is reported to have high potential for mitigating such risks and improving safety outcomes. PtD seeks to assess and reduce workplace hazards during the design phase, minimizing unsafe construction conditions. Despite its potential benefits, the construction industry encounters challenges in effectively utilizing PtD. Thus, the implementation of PtD in the US construction industry is limited, and designers' awareness remains low. This evident lack of utilization warrants further examination of the contributing factors. The goal of this study is to identify and rank PtD utilization barriers in the United States (US) construction industry. This study pinpointed 12 pivotal barriers to PtD implementation through a systematic literature review. These barriers were categorized into industry-, project-, designer-, and client-related domains. Furthermore, they were grouped into three clusters based on their influence on PtD implementation from the most to the least influence, based on an expert matter questionnaire. This study also compared the experts' rankings of the identified barriers with their citation frequencies in the reviewed articles. Among other observations, this study found that the lack of PtD professional training and formal education for project stakeholders negatively impacts the likelihood of PtD utilization and exacerbates several other barriers. Therefore, it is advisable to prioritize addressing this barrier by allocating the necessary resources and efforts to efficiently address it. Construction industry stakeholders with a vested interest in advancing PtD applications are encouraged to leverage the insights this study provides to expedite the adoption of PtD. [ABSTRACT FROM AUTHOR]

In the construction industry, most safety culture studies are limited to a single country, with minimal attention to cross-country studies. This limits creating a foundation for a robust framework and reliable safety culture scale. This study addresses this gap by studying safety culture in 10 countries, including those without previous studies. The survey instrument, completed by 311 construction employees, identified seven key factors measuring safety culture, with content and construct validity ensuring the reliability and validity of survey findings. Results indicated that work experience, education level and employment status have significant impacts on employees’ safety culture. Additionally, similarities and differences in these factors across countries were investigated, and the fatalism and optimism factor and the work pressure and priority factor are the most significant contributors to the weakening of safety culture in the construction industry. This research allows industry practitioners to systematically assess on-site safety culture, oversee practices and improve. [ABSTRACT FROM AUTHOR]

Shield tunnelling presents numerous potential risks particularly in complex geological environments. In this study, we propose a novel fuzzy model for assessing the risk of tunnelling in soil-rock mixed strata. The proposed model incorporates the fuzzy setpair analysis (FSPA) method into fuzzy c-means (FCM) clustering to overcome the limitations of conventional data normalisation. Data pertaining to tunnelling machine, deformation, and vibration are employed to construct an index system using mutual information algorithms for feature selection. The intercriteria importance though intercriteria correlation is employed to weight the indicators, and the FSPA method is adopted to calculate the connection number. Subsequently, the results are classified by the FCM with a modified objective function that considers the importance of risk indicators to derive the risk level of each ring in real time. The proposed model is applied to a case study of a shield tunnelling project in Guangzhou, China. The analysis results indicate a higher risk level from Ring 1572 onwards, which necessitates a judicious regulation of the thrust force and earth pressure. This novel method provides a practical and reliable tool for guiding risk decisions during the tunnel construction. [ABSTRACT FROM AUTHOR]

Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios. [ABSTRACT FROM AUTHOR]

When constructing long-span spatial steel structures, the unformed structure is often incomplete and unstable. The construction sequence significantly influences the mechanical state of the structure during the construction stages (CSs), affecting both the path and time effects. This study examined the mechanical properties of the construction process using an actual project as a case study, comparing two methods: one-step forming and stage-by-stage forming. Critical turning points of stress and displacement during the CSs were identified as the initial installation and unloading stages. Stress concentrations frequently occurred at temporary support points, and peak displacements often appeared at the outer overhanging bars of the structure. A well-planned construction sequence can effectively manage the structure's formation, boundaries, and loading to ensure construction safety and stability. The conclusions and analysis methods from this study provide valuable references for the design and construction of similar long-span spatial steel structures. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

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Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation. [ABSTRACT FROM AUTHOR]

In this article, we discuss the concept of safetywashing defined as the strategic practice of promoting, marketing, and branding of safety practices without full disclosure of negative information to improve the image of the organization. The research seeks to answer two questions: first, what are safetywashing strategies? Second, what are the effects of safetywashing strategies? To study this, 106 news articles relating to construction safety in India, as well as 439 reader comments on them, were systematically collected and their contents analyzed to compile multiple case studies which had evidence of safetywashing. We analyze multiple instances from these case studies to build theoretical insight into these strategies and their effects, using an approach anchored in a social exchange theoretical framework. We highlight different safetywashing strategies employed in the construction sector, such as safety as a project objective, explaining safety initiatives, associating with pioneers, as well as investing in safety. These strategies lead to accepting of organizations, prioritizing safety, and diverting focus, all of which have different implications for safety practice in the construction industry. [ABSTRACT FROM AUTHOR]

Intrusion behavior in hazardous areas is one of the major causes of construction safety accidents including falls from height and strikes by objects. Implementing automatic and preassessment of intrusions to enhance safety performance is of great importance in construction areas. Traditional behavioral safety management mainly relies on manual observation, which makes it difficult to accurately identify detailed changes in behavioral posture, while the results of risk analysis are susceptible to bias due to subjective factors. The emergence of artificial intelligence techniques and computer vision has provided new solutions for human behavior detection in recent years. Accurate vision-based skeleton extraction helps capture detailed behavioral information. Current studies generally focus on intrusion after the occurrence and rarely select metrics considering complex human motion features. It is difficult to accurately assess the potential intrusion risk, resulting in inefficient ex-ante safety management outcomes. This paper presents a novel intrusion assessment approach by integrating human kinematics to extract risk indicators and apply objective assessment methods for risk quantification. An indoor experiment with control groups was conducted by employing skeleton detection technology with safety knowledge to demonstrate its feasibility and effectiveness. The risk levels of the different activities were compared through a control group experimental analysis. The results show that a satisfying accuracy of intrusion assessment can be achieved for different workers. Appropriate warning and intervention methods can be implemented to mitigate the occurrence or reduce the severity of intrusions, thus reducing safety incidents on construction sites. [ABSTRACT FROM AUTHOR]

Purpose: Safety issues have become increasingly important in the construction business. In an effort to reduce accidents, eliminate illness, and provide an ideal working atmosphere on their built-up sites, several construction businesses worldwide are using ecological, health, and safety oversight strategies. Personal Protective Equipment (PPE), specific processes, and engineering safeguards have been the mainstays of traditional prevention of infections methods. Among Hong Kong construction workers, this research examined the linear and curvilinear correlations between gender and safety performance (accident rates and occupational injuries), as well as safety attitudes. Method: This study examines at the connections between psychological stresses (distress and fulfilment with employment on the psychological level), safety performances (self-reported accident and injury rate at workplace), and safety environment (safety attitudes and interactions). 375 construction workers from 27 sites in Hong Kong, China (M = 365, F = 9, mean age = 35.64 years) were given an interview. In-depth interviews and questionnaires were used to gather data between February and May 2018. Results: Using a path analysis conducted using the EQS-5, the proposed model of the interaction among the safety the surroundings, safety performance, and psychological stressors was evaluated. The results provide some support for the idea, as psychological distress and safety attitudes both predict the prevalence of accidents and work-related injuries. The analysis gave important information on eight components of safety in construction, involving security procedures and specifications, safety organization as well, safety training, inspection of hazardous circumstances, personal protection program, machinery and supplies, promotion of safety, and managerial behaviour. Conclusion: The survey findings give managers of building projects and safety in construction professionals with practical information to help them keep their sites safe. This article presents insights and comments. Variability in organisational and individual characteristics can explain a large portion of the diversity in selfprotective conduct in health care environments. Moreover, a mediator in the relationship between safeguarding mentality and the number of accidents was shown to be psychological distress. Examined is the effect of these results on psychological counselling in the construction industry. [ABSTRACT FROM AUTHOR]

Purpose: Health and safety is an important issue in workplaces, and despite safety procedures becoming more strict, serious accidents are still happening within the UK construction sector. This demonstrates poor performance in the implementation of safety procedures on construction sites. One of the key challenges is the unwillingness of the site workforce, especially the subcontracted operatives, to adhere to safety provisions on construction sites. As such, this study investigates the strategies for enhancing safe behaviour amongst subcontracted operatives in the UK construction industry. Design/methodology/approach: The study used exploratory sequential mixed method research, involving interviews and questionnaires as means of data collection, and thematic analysis, reliability analysis and exploratory factor analysis as methods of data analysis. Findings: The study suggests that various carrot and stick measures are expected to be put in place as part of the strategies for enhancing safe behaviour amongst subcontracted operatives. These include adequate enforcement of safety practices by the management, operative engagement and motivation, commendation and rewards, site safety targets, leadership style and motivation. Originality/value: Application of the suggested measures could enhance safety on construction sites, as it provides practical measures and solutions for inculcating safety behaviours amongst the site operatives who are most likely to be the victims of site accidents. [ABSTRACT FROM AUTHOR]

In the construction industry, despite the development of technology and the efforts of companies, safety accidents are frequent, and the types of accidents are also diversified. In particular, when looking at the accident rates of the construction industry, the number of deaths from fall accidents accounts for a very high proportion. To resolve this, various measures to prevent fall, such as installation of safety railings and safety nets, have been proposed at the national level, but the effect is very insignificant. Therefore, it is necessary to establish measures for safety management and to propose prevention techniques by in-depth analysis of the causes of fall accidents through actual accident cases at the construction sites. The purpose of this study is to assess the risk of the cause of fall accidents for sustainable safety management at construction sites. To this end, data collection of fall accident cases at domestic construction sites, risk assessment by cause, and fall accident prevention techniques are conducted in order. This study was conducted on fall accident cases that occurred at a height of more than 2m. The results of this study will contribute to substantially reducing fall accidents at construction sites in South Korea. Additionally, it is used as basic data for improving Korea's construction safety management system.

Accurately detecting whether workers wear Personal Protective Equipment (PPE) in real time plays an important role in safety management. Previous studies mainly used multiple models jointly or only object detection for wearing relationship judgments. This makes it difficult to provide real-time, accurate detection of security relationships. Therefore, this paper proposes safe-wearing detection rules and a novel multi-targets and keypoints detection framework (MTKF), which is capable of accomplishing multiple classes of targets and keypoints detection simultaneously in one-stage, to get more accurate results. In order to improve the performance in the PPE and worker keypoints detection in challenging construction scenes, the detection head transformation strategy, mix group shuffle attention (MGSA) module, and the improved dual and cross-class suppression algorithm (DC-NMS) are proposed. The experimental results are implemented on one established dataset (Joint dataset) and two public datasets (SHWD and COCO), which conduct a comprehensive evaluation in multiple dimensions. Compared to the baseline model, our method improves the mAP by 2.6%–7.1%, reduces the number of parameters by at least 70%, and is able to achieve an inference speed of 155 fps.

AbstractOver the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safety-focused initiatives. Addressing this, this article introduces a novel approach using YOLOv8, a rapid object detection model, for recognizing personal protective equipment (PPE). This method, leveraging computer vision (CV) instead of traditional sensor-based systems, offers an economical, simpler and field-friendly solution. We established the Color Helmet and Vest (CHV) and Safety HELmet dataset with 5K images (SHEL5K) datasets, comprising eight object classes like helmets, vests and goggles, to detect worker-worn PPE. After categorizing the dataset into training, testing and validation subsets, diverse YOLOv8 models were assessed based on metrics including precision, recall and mAP50. Notably, YOLOv8x and YOLOv8l excelled in PPE detection, particularly in recognizing person and vest categories. This innovative CV-driven method promises real-time PPE detection, fortifying worker safety on construction sites.