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Abstract 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.

Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system's performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments. [ABSTRACT FROM AUTHOR]

Limited research exists on safety leadership and safety climate in developing countries, despite their established importance in the construction industry. This study addresses this gap by investigating how immediate superiors' safety leadership behaviours influence safety climate perceptions among construction professionals in southern India. Using a quantitative approach, the study collected valid questionnaire surveys among 279 construction professionals by convenience sampling across various construction sites. The survey revealed that safety leadership significantly and positively predicts five key safety climate factors: management's commitment to safety, safety equipment and procedures, safety training, communication and openness, and group dynamics and safety culture. These findings highlight the critical role immediate superiors play in shaping safety climate perceptions through their commitment, communication, and employee involvement. This research underscores the importance of investing in safety leadership development to improve safety outcomes, reduce accidents and injuries, and enhance regulatory compliance within the Indian construction industry. [ABSTRACT FROM AUTHOR]

The cumulative length of subways and tunnels in China has reached nearly 25 000 kilometers, bringing huge demand for underground construction. The monitoring for construction safety has become more significant, due to complicated environment and rapid-onset natural disasters, which raises the frequency of accidents in underground construction. The informatization engineering of monitoring and early-warning on natural disasters is built to achieve construction safety, through promoting integrated monitoring on multi-hazard and disaster chains, and practicing intelligent algorithms on comprehensive quick accurate perception and early-warning of construction hazards. The integrated network architecture for intelligent safety monitoring with edge-computing ability is established, realizing the visualization of monitoring data analysis through researches on AD hoc transmission based on directional antenna network bridge, development of IoT edge computing software and researches on accessing to IoT platform for multiple types of sensors, and development of the front-end and back-end of the monitoring system. The introduction of IoT and cloud computing sustains accessing to platform for huge amount of sensors and full exploiting of big-data mining, improving intelligent safety hazard identification, and reducing safety risks in construction. Based on the instant response of edge devices and interval data acquisition of sensors, and the capability for more than 100 000 sensors to access the system simultaneously, the complete monitoring system for underground constructions with second-level warning is established. [ABSTRACT FROM AUTHOR]

Construction scholars and practitioners have identified a repetitive pattern of direct causes leading to both fatal and non-fatal injuries among construction workers. Over the years, direct causes such as falls, electrocutions, and being struck have consistently represented a substantial proportion of recorded and reported injuries in the United States. One potential factor contributing to this repetition is the absence of root cause investigations for incidents. Incident investigations should focus on system deficiencies and shortcomings instead of individual behaviors. While the identification of incident root causes provides the needed information to eliminate the direct causes, it is inherently complex. Recently, the use of tailored incident investigation protocols as a practical and systematically conducted method was suggested to uncover the root causes of incidents, subsequently assisting in reducing their recurrence. To illustrate the feasibility of such an approach, this article provides a step-by-step guide to creating a tailored investigation protocol for revealing the root causes of arc flash incidents by utilizing a panel of safety experts. In addition, this study demonstrates the feasibility of developing tailored investigation protocols for other common causes, such as falls and electrocutions. Tailored investigation protocols streamline the identification of potential root causes to a manageable number, relying on subject matter experts. Consequently, they enhance learning from incidents by mitigating investigators' biases and potential lack of experience. Safety practitioners can use the method presented in this article to create tailored investigation protocols based on their working environment to improve learning for occupational injuries. [ABSTRACT FROM AUTHOR]

Construction safety is always an issue for construction workers. Hence, personal protective equipment plays a critical role in avoiding potential hazards on construction sites. Among these, hard hats protect against head contact with falling objects on construction sites. This study aims to examine the effects of hard hats with different peak lengths on the field of view at different angles in the upward part and to examine the effects of possible field of view losses caused by the hard hat on the reaction times of the workers. A questionnaire was designed and administered to the construction workers. Experiments were then conducted with a group of subjects to assess their peripheral vision level as well as reaction times. The study found that peripheral vision is affected by the peak size of hard hats. The study further revealed that there is a significant relationship between reaction times and hard hat peak size. [ABSTRACT FROM AUTHOR]

Construction safety is of significance since construction accidents can result in loss of property and large numbers of casualties. This research aims to identify the critical causes of construction accidents by introducing a hybrid approach. The hybrid approach is developed to identify the critical causes of construction accidents by combining the human factors analysis and classification system (HFACS) model with complex network (CN) theory. A total of 863 construction accident cases were collected, and 46 causal factors were identified. Subsequently, the accident causal network was established, and six critical causal factors were extracted. The hybrid analysis approach is demonstrated with a real construction accident case, and the results demonstrate that the hybrid approach could better identify the critical causal factors. Consequently, this research enables the enhancement of understanding the HFACS framework and CN theory, as well as a contribution to safety management in the construction industry at different levels. [ABSTRACT FROM AUTHOR]

Wearable biosensors, in conjunction with machine learning, have been employed to develop less invasive monitoring techniques for assessing stress among construction workers during fieldwork. However, existing techniques face limitations in terms of scalable field application due to their subject and context dependency; it is difficult to apply them to new people in new contexts without additional labeled data collection. Therefore, this study developed a stress detection technique that incorporates domain adaptation, simultaneously learning a classifier and a subject- and context-independent features, in this way advancing generalizability. The proposed technique consistently demonstrated superior accuracy compared with benchmarks in classifying stress levels within a testing data set whose subjects and contexts were different from those of training data sets. Thus, the technique can advance generalizability across subjects and contexts. This finding can help us to reliably detect stress for new people in new contexts without additional labeled data collection, thereby contributing to scalable field application of wearable-based stress monitoring at construction sites. [ABSTRACT FROM AUTHOR]

A safety plateau in the construction industry has been reported in the US and Canada, which has prompted researchers to seek new factors affecting construction safety performance. Tapping into advancements in the theory of human and organizational behaviors can yield valuable new perspectives. Therefore, by leveraging the advancement of the Job Demand Control Support model in the field of occupational safety and health, this paper firstly tested the impact of one newly added hindrance stressor (i.e., interpersonal conflicts on construction sites) by researchers on organizational behaviors on the safety performance of construction workers, based on two cross-sectional studies in the US and Canada. Differentiations were made between conflicts with supervisors and conflicts with coworkers. One personal resource factor, i.e., individual resilience, was also considered in this paper. A "causal" chain that shows the mitigation impact of individual resilience on conflicts with supervisors or coworkers, and the adverse impact of conflicts with supervisors or coworkers, on unsafe events were found to hold true for both US and Canadian construction sites, based on the results from measurement invariance tests and structural equation modelling. Recommendations regarding how to improve construction workers' individual resilience and reduce interpersonal conflicts on site, thereby reducing safety incidents on site, are provided. [ABSTRACT FROM AUTHOR]

The complexity of the wharf components and the harshness of the offshore construction environment increase the safety risk of hazards, which has highlighted the importance and urgency of safety risk management in high-pile wharf constructions. This paper established a visualized digital construction safety risk model for high-pile wharf based on a so-called FAHP method (the combination of fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) methods). The construction safety risk indicators were constructed as the target layer, the principle layer and the scheme layer, and then the corresponding safety risk assessment algorithm was established. The physical, functional and safety risk assessment parameters of the component in the BIM model were employed to the safety risk assessment algorithm, and the risk assessment level of each sub-process was subsequently classified. The case study indicated that the high-pile wharf construction project included five elements in principle layer and 15 risk indicators in the scheme layer. Moreover, it was demonstrated that the sub-processes with the highest construction risk level were steel pipe pile sinking in wharf construction and steel pipe pile, steel sheath-immersed pile sinking and embedded rock pile construction in approaches to bridge construction with a risk level of III. In this way, the quantitative visualization of the construction safety risk was effectively realized, which facilitates the safety risk management of construction sites and timely warning and response to unexpected safety accidents. [ABSTRACT FROM AUTHOR]

The construction industry is fraught with hazards, chief among them being the risk of falls from elevated positions, which are a leading cause of both fatalities and injuries among workers. Despite the prevalence of traditional safety training methods, their effectiveness in reducing fall risks remains limited. To solve this issue, this study proposes the adoption of interactive VR technologies to provide construction workers with immersive training experiences in the critical domain of fall safety. This approach not only ensures adherence to the Occupational Safety and Health Administration (OSHA) requirements but also leverages VR's immersive capabilities to create a comprehensive and effective learning tool. To evaluate the proposed system's efficacy, the researchers conducted an empirical assessment involving eighty-two construction workers from two small enterprises. Participants were divided into two groups: one receiving traditional training and the other undergoing VR-based instruction. Both groups underwent pre- and post-training evaluations comprising six targeted questions designed to measure the impact of each training method on their understanding and awareness of fall safety practices. The comparative analysis revealed no significant differences in baseline knowledge between the two groups prior to the training interventions. However, post-training evaluations demonstrated a notable improvement in the VR group, with a significant decrease in the number of incorrect responses, in stark contrast to the group subjected to traditional training methods. Statistical analysis further confirmed the superiority of VR training in enhancing participants' knowledge. This was quantified by a p- value of 0.0016, indicating a high level of statistical significance well below the conventional threshold of 0.05. This study highlights the significant advantages of VR technology in construction safety training, demonstrating its superiority over traditional training methods in terms of knowledge retention and practical application. The results strongly support the wider adoption of VR in safety training, indicating its potential to enhance safety outcomes in the construction sector. [ABSTRACT FROM AUTHOR]

Safety performance of the construction industry in the US has been a concern among the industry practitioners and researchers. Despite all the efforts, the number of construction workplace fatalities has increased in the last decade. Beside loss of lives, the project stakeholders suffer greatly because of the financial burden imposed as a result of occupational injuries and accidents. In order to address the problem, recent studies have turned their attention to the more proactive approaches, such as assessing workers' perceptions of safety. The present study focused on measuring workers' perceptions of safety on construction projects with three distinct leading indicators such as safety climate, safety control, and risk perception. The link between workplace safety performance with the aforementioned indicators has been separately examined in existing studies. This study explored the interrelationships among the indicators. Correlation analyses between the variables demonstrated a positive correlation between workers' perceptions of safety control and safety climate. As workers' perceptions of safety climate depend on various factors, the safety programs can target to improve those individual factors and in turn improve the overall safety climate. [ABSTRACT FROM AUTHOR]

The construction industry is vulnerable to heat-related hazards, necessitating identification of high-risk areas and contributing factors. This study introduces a novel framework that integrates microclimate simulations with geometric modeling, focusing on the often-underestimated role of heat radiation in assessing heat-related hazards in construction environments. By analyzing 2 years of meteorological data from a construction site in College Station, Texas, this research uncovers the inadequacies of the heat index (HI), a widely recognized thermal-physiological model in the US construction sector. Compared with the wet bulb globe temperature (WBGT), the HI displayed notable variations. Specifically, out of 1,719 data points labeled as danger by HI, WBGT recategorized them as low risk (n=62), moderate risk (n=1,264), high risk (n=300), and extreme risk (n=93). These discrepancies are predominantly associated with the influence of heat radiation. Furthermore, this study emphasizes the importance of accounting for the spatially varying nature of heat radiation in construction environments, influenced by factors such as adjacent structure height, surface materials, and shading patterns. The research highlights the need for monitoring site-specific heat radiation and its potential impact on workers' health and safety. Overall, the findings contribute to our understanding of heat-related hazards in construction and offer valuable insights for developing more effective heat-related safety management strategies. [ABSTRACT FROM AUTHOR]

The safety risk analysis of urban elevated bridge construction is an important management method to reduce the loss of safety accidents, and it has significant scientific research value and engineering application value. Therefore, this study proposes a novel analysis method to address these challenges. Firstly, this paper constructs a Work Breakdown Structure (WBS)–Risk Breakdown Structure (RBS) matrix for the safety risk of urban elevated bridge construction in order to achieve a comprehensive and complete identification of the indicator system. Then, a combination of static weights and dynamic weights calculation methods is developed. The static weights are obtained using the analytic hierarchy process, while the dynamic weights are obtained based on the relationship between the dynamic scores of construction safety risk indicators in different construction stages and the preset evaluation levels. Finally, a case study of the Longlingshan elevated bridge project in Wuhan, China, is conducted to validate the feasibility of the proposed model and its potential application in projects. The case analysis for the first time reveals that with the progress of construction, the weights of each indicator continuously change, and the secondary indicators related to environmental factors, such as extreme high-temperature weather, undergo the greatest changes. A comparison of different dynamic weight calculation methods is conducted to highlight the advancement of the proposed model. The research findings of this paper will provide new insights and guidance for improving the construction safety of urban elevated bridge projects. [ABSTRACT FROM AUTHOR]

Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as ignoring non-desired outputs and slack variables, the efficiency values being limited to the (0, 1) range, and a narrow perspective. To address these shortcomings, this study focuses on the characteristics of the construction industry and introduces the Super-SBM model and Malmquist index into the assessment of safety efficiency in the construction industry. The study analyzes the evolution characteristics of safety efficiency from both static and dynamic perspectives. Furthermore, using panel quantile regression models, the study identifies the factors influencing safety efficiency and analyzes their heterogeneity. Analyzing panel data from 30 provinces in China from 2015 to 2021, the results show that the overall safety efficiency of the construction industry in China is relatively low, with noticeable spatial clustering characteristics. Provinces in the eastern and central regions exhibit higher levels of construction safety efficiency. The Malmquist index demonstrates a declining trend, with technical efficiency being the primary factor limiting the improvement of safety efficiency in construction. Factors such as per capita GDP, urbanization rate, committed contract amounts, and the number of professionals engaged in survey and design, as well as engineering supervision, have an impact on construction safety efficiency, and the effects of these variables vary across different quantile levels of safety efficiency. This research can assist decision-makers in gaining a better understanding of the safety conditions in different regions of the construction industry. It can also assist in developing customized policies to enhance the health and safety environment, thereby promoting the stable development of the construction industry. [ABSTRACT FROM AUTHOR]

Purpose: Tactile communication that relies on the human sense of touch replicated using vibration motors is increasingly being explored for seamless communication on construction jobsite. However, the technological efficacy cannot secure the users' acceptability of the tactile communication devices. This study aims to assess the factors affecting the wearability of such a portable tactile device based on the responses from practicing professionals. Design/methodology/approach: The investigation adapted a three-step phenomenological interviewing approach to seek feedback from construction personnel in Texas, the USA, regarding the viability of wearable tactile communication. The interviewees expressed various opinions about the on-body placement upon exposure to a portable tactile feedback prototype developed for this study, which was used to derive inferences regarding the factors affecting its on-field acceptability. Findings: All the participants of the round-table study (11 out of 11) considered tactile feedback as a viable mode of communication on construction jobsite. Seven professionals supported the integration of a tactile device with the hard hat, whereas the rest preferred tactile eyeglasses. Weatherability, rechargeability, traceability, safety and social receptivity were identified as the major factors affecting the on-body placement of the wearable tactile communication device. Originality/value: This paper presents a roadmap to gain construction industry opinion on the factors that can affect the on-body placement of a wearable tactile communication device. The five aforementioned factors impacting tactile communication acceptability were used to evaluate 10 potential on-body placements. The findings have implications for research and development of wearable tactile devices and the subsequent acceptability of such a device on the jobsite. [ABSTRACT FROM AUTHOR]

The construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole. The existing research on construction accidents heavily relies on expert evaluations, which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity. However, expert opinions provided in construction accident reports offer a valuable source of knowledge that can be extracted and utilized to enhance safety management. Today this valuable resource can be mined as the advent of artificial intelligence has opened up significant opportunities to advance construction site safety. Ontology represents an attractive representation scheme. Though ontology has been used in construction safety to solve the problem of information heterogeneity using formal conceptual specifications, the establishment and development of ontologies that utilize construction accident reports are currently in an early stage of development and require further improvements. Moreover, research on the exploration of incorporating deep learning methodologies into construction safety ontologies for predicting construction safety incidents is relatively limited. This paper describes a novel approach to improving the performance of accident prediction models by incorporating ontology into a deep learning model. First, a domain word discovery algorithm, based on mutual information and adjacency entropy, is used to analyze the causes of accidents mentioned in construction reports. This analysis is then combined with technical specifications and the literature in the field of construction safety to build an ontology encompassing unsafe factors related to construction accidents. By employing a Translating on Hyperplane (TransH) model, the reports are transformed into conceptual vectors using the constructed ontology. Building on this foundation, we propose a Text Convolutional Neural Network (TextCNN) model that incorporates the ontology specifically designed for construction accidents. We compared the performance of the TextCNN model against five traditional machine learning models, namely Naive Bayes, support vector machine, logistic regression, random forest, and multilayer perceptron, using three different data sets: One-Hot encoding, word vector, and conceptual vectors. The results indicate that the TextCNN model integrated with the ontology outperformed the other models in terms of performance achieving an impressive accuracy rate of 88% and AUC value of 0.92. [ABSTRACT FROM AUTHOR]

[Objective] Worker′s unsafe behavior is the fundamental factor in urban rail transit construction accidents. As the traditional management mode is insufficient in restraining the workers from the unsafe behavior, it is necessary to eliminate the hidden danger of accidents subjectively with the help of high precision positioning and intelligent identification technologies. [Method] The generation mechanism of worker′s unsafe behavior in urban rail transit is introduced. In combination with the technologies of UWB (ultra-wideband) high precision positioning, camera self-calibration and intelligent identification based on convolutional neural network algorithm, an integrated intelligent management platform with functions of positioning, perception, identification, early warning and communication is built. Taking helmet identification as an example, the topology flow chart of helmet identification is constructed, and the algorithm of worker′s unsafe behavior identification based on convolutional neural network is tested. [Result & Conclusion] The test results show that the algorithm can identify the person who does not wear safety helmet on construction site, verifying its accuracy. The technology realizes intelligent identification and early warning of worker′s unsafe behavior in urban rail transit construction.

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

Abstract Background The risk of high-energy trauma injuries on construction sites is relatively high. A delayed response time could affect outcomes after severe injury. This study assessed if an advanced first aid course for first aid response for laypersons (employees or apprentices) in the construction industry or real-time video communication and support with ambulance personnel, or neither, together with access to an advanced medical kit, would have an effect on immediate layperson vital responses in a severe injury scenario. Method This was a controlled simulation study. Employees or apprentices at a construction site were recruited and randomly allocated into a group with video support or not, and advanced first aid course or not, and where one group had both. The primary outcomes were correct behavior to recognize and manage an occluded airway and correct behavior to stop life-threatening bleeding from a lower extremity injury. Secondary outcomes included head-to-toe assessment performed, placement of a pelvic sling, and application of remote vital signs monitors. Results Ninety participants were included in 10 groups of 3 for each of 4 exposures. One group was tested first as a baseline group, and then later after having done the training course. Live video support was effective in controlling bleeding. A first aid course given beforehand did not seem to be as effective on controlling bleeding. Video support and the first aid course previously given improved the ability of bystanders to manage the airway, the combination of the two being no better than each of the interventions taken in isolation. Course exposure and video support together were not superior to the course by itself or video by itself, except regarding placing the biosensors on the injured after video support. Secondary results showed an association between video support and completing a head-to-toe assessment. Both interventions were associated with applying a pelvic sling. Conclusion These findings show that laypersons, here construction industry employees, can be supported to achieve good performance as first responders in a major injury scenario. Prior training, but especially live video support without prior training, improves layperson performance in this setting.

The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. First, the original data are optimized via the SSA, which has global optimization capability, high noise immunity, and high adaptivity. The original signal is subsequently decomposed into multiple subsignal sequences via a variational modal decomposition (VMD) algorithm combined with a rolling decomposition technique. Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. The prediction accuracy of different models was analyzed via the measured settlement data during the construction period of the Beijing subway as an example. The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.

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.

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.

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.

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.

Highway work zones are essential for the preservation and improvement of the national road system. Nevertheless, these areas are reported to be among the most hazardous workplaces. Thus, it is crucial to develop appropriate measures to effectively mitigate the safety risks, which require a good understanding of the critical causes of accidents. While there are many previous studies on critical causes of construction accidents, none of them was specifically focused on highway work zones. This type of construction workplace has its own characteristics (e.g., near-passing traffic), which can lead to a unique set of critical causes of accidents. This study used text mining to extract root causes from a large narrative data set of construction accidents at work zones obtained from the Occupational Safety and Health Administration (OSHA). The study applied latent Dirichlet allocation (LDA) modeling on the text corpus to extract 12 root causes, which were subsequently classified into five groups: management, human, unsafe behavior, environmental, and material factors. In addition, social network analysis (SNA) was conducted to gain further insights into the interrelations between the root causes to determine their criticality degree. As a result, four highly ranked causes were identified: supervision dereliction of duty, weak safety awareness, poor construction environment, and risk-taking behavior. The findings of this study offer a new understanding of critical factors that highway agencies and contractors should focus on when developing construction accident prevention strategies at work zones. [ABSTRACT FROM AUTHOR]

Safety academics and practitioners in construction typically use safety prediction models that employ information associated with past incidents to predict the likelihood of future injury or fatality on site. However, most prevailing models utilize only information related to failure (i.e., incident), so they cannot distinguish effectively between success and failure without well-informed comparison. Furthermore, recordable incidents on construction sites are extremely rare, which results in data that are too sparse to make predictions with high statistical power. This paper empirically reviews different approaches to safety to increase the understanding of conditions associated with safety success and failure. Empirical data about business-, project-, and crew-related factors were collected to predict serious injury and fatality (SIF) exposure conditions. A variety of modeling techniques were tested in a machine learning pipeline to identify the most accurate and stable predictive models. Results showed that the multilayer perceptron (MLP) approach best distinguished SIF exposure conditions from safety success conditions using nonlinear decision boundaries. The most influential factors in the models included the crew experience working together, supervisor experience with the crew, total number of workers under the supervisor's purview, and the maturity of leadership development programs for frontline supervisors. This study showed that data sets with both success and failure information yield more reliable and meaningful predictions than data sets with failure alone. Such an approach to safety data collection, analysis, and prediction could be used by future researchers to generate new insights into the causes of serious incidents and the relationships among causal factors. [ABSTRACT FROM AUTHOR]