Construction Safety Management Research Articles

Safety management in the construction industry: Bibliometric analysis

Despite the continuous improvement of safety standards in the construction industry, this sphere remains one of the most hazardous sectors, and safety management is becoming a crucial determinant of effective operation at construction sites. The aim of this study is to identify the essential contextual, evolutionary-chronological, and geographical-industrial scientific landscape of the development of safety management in the construction industry through comprehensive bibliometric analysis. More than 15,000 articles indexed by Scopus from around the world from 2000 to 2023 were analyzed using the VOSviewer tool, filtered by keywords such as “construction,” “safety,” and “management.” The research revealed the intensification of publishing activity since 2012 (with peak growth in 2012, 2020, and 2022). The leading scientists are from China, the United States, and the United Kingdom (e.g., Chinese scientists make up 26.1% of all scholars). The majority of papers are in the field of engineering. Priority areas of research include safety climate, safety behavior, and building information modeling. The closest connections of construction safety management are with the study of professional risks, safety techniques, and quality control. The evolution of research focuses on “human health and safety-risks-digital security systems.” In general, the findings of this study provide a foundation for future research aimed at enhancing safety management in the construction field, potentially increasing worker protection and technical operational efficiency.

Proactive safety hazard identification using visual–text semantic similarity for construction safety management

Automated safety management in construction can reduce injuries by identifying hazardous postures, actions, and missing personal protective equipment (PPE). However, existing computer vision (CV) methods have limitations in connecting recognition results to text-based safety rules. To address this issue, this paper presents a multi-modal framework that bridges the gap between construction image monitoring and safety knowledge. The framework includes an image processing module that utilizes CV and dense image captioning techniques, and a text processing module that employs natural language processing for semantic similarity evaluation. Experiments showed a mean average precision of 49.6% in dense captioning and an F1 score of 74.3% in hazard identification. While the proposed framework demonstrates a promising multi-modal approach towards automated safety hazard identification and reasoning, improvements in dataset size and model performance are still needed to enhance its effectiveness in real-world applications.

Use of Knowledge Graphs for Construction Safety Management: A Systematic Literature Review

Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research.

A data-driven and knowledge graph-based analysis of the risk hazard coupling mechanism in subway construction accidents

With the development and expansion of subway construction, accidents usually cause economic losses and seriously threaten personnel safety. Exploring the experience and lessons from accident reports is beneficial for promoting subway construction safety management. Therefore, this study proposes a data-driven and knowledge graph-based approach to reveal the coupling mechanism of risk hazards in subway construction. A total of 231 subway construction accident reports in China from 2001 to 2023 are collected as the database of the knowledge graph. By developing a subway construction safety accident knowledge graph (SCSAKG), the potential paths for accidents can be revealed. To further investigate the internal features of the knowledge graph, topological indicators are proposed to explore the relationships between hazards, hazard types, hazard frequencies, hazard occurrence times, accidents, and accident consequences. Prevention strategies for key risk hazards in subway construction are developed based on the proposed coupling mechanism. The proposed methods that combine knowledge graphs with accident reports can provide decision-making references for subway construction safety management.

Влияние химического фактора на безопасные условия труда машинистов дорожно-строительной техники

The article considers current issues of the impact of harmful and hazardous factors of the production environment and working process on the working conditions of employees of the road construction industry. In particular, potentially negative chemical factors at the workplace of drivers of road construction machinery have been identified; the extent of their impact on the personnel has been determined based on the special assessment of working conditions. The list of harmful substances and mixtures in the air of the working area of the drivers of road construction machinery has been established. The protocols of chemical factor measurement and assessment and the cards of special assessment of working conditions at 16 workplaces of the drivers of various road construction machinery have been analyzed. Based on the comparative analysis and its graphic visualization, specific working conditions at the workplace of drivers most exposed to the adverse impact of chemical factors have been detected. The largest emissions of harmful chemicals are primarily specific for the workplaces of drivers of highly specialized road equipment, i.e., pavers, self-propelled rollers, and motor graders. Therefore, at the workplace of a driver of a GS-1402 motor grader, the maximum permissible concentration of chemical elements is exceeded by 11 % for NO2 and СО combination. For asphalt paver drivers, when employing Vogele and DC 191.504.02 pavers, the shift-average maximum permissible concentrations of harmful substances are exceeded by 9.67 % and 4.33 % respectively for the mixture of limiting aliphatic hydrocarbons C1–C10. The study of chemical impact on road construction workers can aim at the detailed investigation of working conditions and their improvement by the development of workflow organization at all stages of motor road construction. The special assessment of working conditions can be used as a tool to develop approaches to security and occupational safety management in road construction.

Experimental Study on Using Synthetic Images as a Portion of Training Dataset for Object Recognition in Construction Site

The application of Artificial Intelligence (AI) across various industries necessitates the acquisition of relevant environmental data and the implementation of AI recognition learning based on this data. However, the data available in real-world environments are limited and difficult to obtain. Construction sites represent dynamic and hazardous environments with a significant workforce, making data acquisition challenging and labor-intensive. To address these issues, this experimental study explored the potential of generating synthetic data to overcome the challenges of obtaining data from hazardous construction sites. Additionally, this research investigated the feasibility of hybrid dataset in securing construction-site data by creating synthetic data for scaffolding, which has a high incidence of falls but low object recognition rates due to its linear object characteristics. We generated a dataset by superimposing scaffolding objects, from which the backgrounds were removed, onto various construction site background images. Using this dataset, we produced a hybrid dataset to assess the feasibility of synthetic data for construction sites and to evaluate improvements in object recognition performance. By finding the optimal composition ratio with real data and conducting model training, the highest accuracy was achieved at an 8:2 ratio, with a construction object recognition accuracy of 0.886. Therefore, this study aims to reduce the risk and labor associated with direct data collection at construction sites through a hybrid dataset, achieving data generation at a low cost and high efficiency. By generating synthetic data to find the optimal ratio and constructing a hybrid dataset, this research demonstrates the potential to address the problems of data scarcity and data quality on construction sites. The improvement in recognition accuracy of the construction safety management system is anticipated, suggesting that the creation of synthetic data for constructing a hybrid dataset can reduce construction safety-accident issues.

Occupational Safety and Health Implementation Model in Construction Safety Management System Based on PUPR Minister Regulation Number 10 Of 2021 to Support Infrastructure Development in East Java Province

Purpose: This study aimed to assess the application of occupational safety and health in the construction safety management system and develop a framework for implementing it based on PUPR ministerial regulation Number 10 of 2021, to support infrastructure development in East Java Province. Method: This study used a qualitative method with a phenomenological approach to gather detailed descriptions of participants' impressions, opinions, and views. It applied Alfred Schutz's approach to establish statements and causal relationships between themes. The research involved six participants and took place at the Department of Public Housing in East Java Province. Results and conclusion: The results and conclusions of the research unveil (1) 8 groups of themes and meanings shaping the implementation of occupational safety and health within the construction safety management system, rooted in PUPR Ministerial Regulation Number 10 of 2021 to bolster infrastructure development in East Java Province. (2) These findings are consolidated into 8 major propositions, leading to the construction of a comprehensive model for implementing occupational safety and health aligned with PUPR Ministerial Regulation Number 10 of 2021. Research implications: The findings of this study can help construction companies and policymakers in East Java Province and beyond improve safety measures. Understanding the framework developed here can lead to safer work environments for construction workers. The qualitative methods used in this research also add to the toolbox of qualitative research methods, providing effective ways to capture subjective experiences. Originality/value: This study offers a unique framework for implementing safety measures in construction projects, specifically designed for the East Java Province and based on relevant regulations. By bringing together various themes from qualitative data, it presents new perspectives on applying regulatory frameworks. Additionally, the use of Alfred Schutz's approach enhances our understanding of how regulations, organizational practices, and individual experiences interact to ensure safety.

EVALUATION OF SAFETY MANAGEMENT DURING TUNNELS CONSTRUCTION IN SAUDI ARABIA

Tunnel construction projects are complex engineering projects that pose numerous safety challenges. Accordingly, prioritizing the protection of workers, the public, and the environment is imperative. This study aimed to comprehensively analyze the most significant risks associated with tunnel construction projects. Twenty-nine hazards were identified through site inspections and interviews with engineers and managers engaged in various tunnel projects in Saudi Arabia. These risks were categorized into six groups, namely, technical, geological, environmental, social, human, political, contractual, and cost-related risks. Data were acquired from 98 of 108 respondents completing a questionnaire. The data were statistically analyzed using the SPSS V20 package. The Total Importance Index Impact (TIII) application determined the most crucial risks affecting tunnel construction safety management. The findings demonstrate that risk analysis facilitates project managers in identifying and focusing on principal risks, which supports the implementation and evaluation of safety management plans. By providing practical guidelines, risk analysis enhances the success rate of tunnel construction projects. Implementing measures to mitigate exposure to elevated noise levels, air supply hazards, fires, and oxygen deficiency is anticipated to result in numerous benefits, including improved worker health and performance, reduced time and expenses, enhanced productivity, and an improved reputation among staff and clients.

Construction safety management based on building information modeling technology

With the progression of society, urban construction has been rapidly advancing, yet the incidence of construction safety accidents has surged. In order to effectively prevent construction safety incidents, construction safety management has become increasingly significant. To achieve the safety management of construction projects, Building Information Modeling (BIM) technology is introduced. It can help construction safety management departments better understand every link in the entire construction process. In this paper, an integrated analysis of BIM technology and the current state of construction safety is presented, aiming to explore the problems existing in the current construction safety management and provide construction personnel and management staff with a comprehensive understanding of predictable risk factors, effective preemptive measures, safety inspections, identification of safety hazards, and other associated issues, thereby guaranteeing the occupational safety and security of construction personnel and property. Consequently, the accident prevention model undergoes a transformation from a reactive approach to a proactive one.

A Systematic Review of Automation Technologies used in Construction Safety Management

A Systematic Review of Automation Technologies used in Construction Safety Management

How to reduce construction risks in rural areas: an evolutionary game analysis

PurposeThe construction industry is widely recognized as one of the most hazardous sectors in the world. Despite extensive research on safety management, a critical issue remains that insufficient attention is devoted to safety practices in rural areas. Notably, accidents frequently occur during the construction of rural self-built houses (RSH) in China. Safety management tends to be overlooked due to the perceived simplicity of the construction process. Furthermore, it is essential to acknowledge that China currently lacks comprehensive laws and regulations governing safety management in RSH construction. This paper aims to analyze the behavior of key stakeholders (including households, workmen, rural village committee and the government) and propose recommendations to mitigate safety risks associated with RSH construction.Design/methodology/approachThis paper applies evolutionary game theory to analyze the symbiotic evolution among households, workmen and rural village committee, in situations with or without government participation. Additionally, numerical simulation is utilized to examine the outcomes of various strategies implemented by the government.FindingsWithout government participation, households, workmen, and rural village committee tend to prioritize maximizing apparent benefits, often overlooking the potential safety risks. Numerical simulations reveal that while government involvement can guide these parties towards safer decisions, achieving the desired outcomes necessitates the adoption of reasonable and effective strategies. Thus, the government needs to offer targeted subsidies to these stakeholders.Originality/valueConsidering that during the construction phase, stakeholders are the main administrators accountable for safety management. However, there exists insufficient research examining the impact of stakeholder behavior on RSH construction safety. This study aims to analyze the behavior of stakeholders about how to reduce the safety risks in building RSH. Thus, the authors intend to contribute to knowledge in this area by establishing evolutionary game model. Firstly, this study carried out a theoretical by using tripartite evolutionary game to reveal the reasons for the high safety risk during building RSH. Practically, this research points out the important role of households, workmen and rural village committee in improving safety management in rural areas. Besides, some suggestions are proposed to the government about how to reduce construction safety risks in rural areas.

The relationship between construction workers’ emotional intelligence and safety performance

PurposeConstruction enterprises are achieving the goal of production safety by increasingly focusing on the critical factor of “human” and the impact of individual characteristics on safety performance. Emotional intelligence is categorized into three models: skill-based, trait-based and emotional learning systems. However, the mechanism of action and the internal relationship between emotional intelligence and safety performance must be further studied. This study intends to examine the internal mechanism of emotional intelligence on safety performance in construction projects, which would contribute to the safety management of construction enterprises.Design/methodology/approachA structural equation model exploring the relationship between emotional intelligence and safety performance is developed, with political skill introduced as an independent dimension, situational awareness presented as a mediator, and management safety commitment introduced as a moderator. Data were collected by a random questionnaire and analyzed by SPSS 24.0 and AMOS 26.0. The structural equation model tested the mediation hypothesis, and the PROCESS macro program tested the moderated mediation hypothesis.FindingsThe results showed that construction workers' emotional intelligence directly correlates with safety performance, and situational awareness plays a mediating role in the relationship between emotional intelligence and the safety performance of construction workers. Management safety commitment weakens the positive predictive relationships between emotional intelligence and situational awareness and between emotional intelligence and safety performance.Originality/valueThis research reveals a possible impact of emotional intelligence on safety performance. Adding political skills to the skill-based model of emotional intelligence received a test pass. Political skill measures the sincere and cooperative skills of construction workers. Using people as a critical element plays a role in the benign mechanism of “Emotional Intelligence – Situational Awareness – Safety Performance.” Improving emotional intelligence skills through training, enhancing situational awareness, understanding, anticipation and coordination and activating management environment factors can improve safety performance. Construction enterprises should evaluate and train workers' emotional intelligence to improve workers' situational awareness and safety performance.

Establishment of Safety Management Measures for Major Construction Workers through the Association Rule Mining Analysis of the Data on Construction Accidents in Korea

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity.

Near-Miss Detection Metrics: An Approach to Enable Sensing Technologies for Proactive Construction Safety Management

One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems.

A hybrid deep semantic mining method considering fuzzy expressions for the automatic recognition of construction safety hazard information

Safety hazards are a key consideration in construction management. The efficient recognition of safety hazard information can help managers formulate safety hazard management measures and improve the efficiency of construction safety management. However, construction site safety hazard data are stored in semistructured and unstructured text formats, which cannot be directly converted into understandable and usable information. Moreover, safety hazard text contains many fuzzy expressions, thereby increasing the difficulty of text semantic analysis; thus, how to accurately mine safety hazard information from complex and diverse text data is an urgent problem that must be solved. In consideration of this problem, we propose a bidirectional long short-term memory (BiLSTM) method with a fuzzy word vector and self-attention mechanism (FSABiLSTM) to automatically recognize safety hazard information. This method adopts TextRank and Word2vec to calculate the fuzzy word vector and process fuzzy expressions in safety hazard text. The safety hazard text semantic features are deeply extracted based on BiLSTM and a fuzzy word vector, and the extracted semantic features are analyzed via a self-attention mechanism. Actual construction safety hazard text is used to verify the reliability and applicability of the method, and the results indicate that the accuracy of this method, which outperforms existing machine learning methods, is 91.70%. In addition, the FSABiLSTM method can be used to automatically evaluate the risk degree of safety hazards; this use is beneficial to managing and controlling safety hazards. Concerning safety hazard text data, this study provides a new deep mining approach that can enhance safety management efficiency.

The use of building information modelling (BIM) in the management of construction safety: The development towards automation hazard identification and assessment

The construction industry faces safety management challenges that impact its reputation and workforce well-being. Despite existing safety regulations, safety violations persist, necessitating innovative solutions. This thesis explores the potential of Building Information Modeling (BIM) as a tool to improve safety practices throughout the project lifecycle. A literature review identifies key safety factors and BIM features, revealing their interconnections and potential for synergistic improvements. A questionnaire survey and semi-structured interviews with 55 construction professionals from diverse regions revealed that over 45% of respondents reported positive outcomes from BIM implementation in their projects. A comprehensive framework is developed to integrate BIM-based safety improvement strategies into various project phases, providing practical guidelines and recommendations for construction professionals to enhance safety practices effectively and efficiently. The research emphasizes the significance of BIM adoption in the construction industry, promoting better safety outcomes and elevating safety management practices. The proposed framework offers valuable insights for stakeholders, encouraging the widespread adoption of BIM technology and its seamless integration with safety practices for improved project safety and overall construction efficiency.

Perencanaan keselamatan, kontrol keselamatan dan kinerja keselamatan

Abstract: Despite the fact that lean construction (LC) has been shown to increase construction safety, little research has been done been carried out to investigate how LC technology can be applied to safety management. This the study aims to close this gap by developing a safety planning and control (SPC) process based on LC technology and evaluate it using a structural equation model. The process is based on the final planner system research framework (LPS) and consists of four sub-systems (ie, analysis layer, planning layer, control layer, and performance layer). Data was collected from 150 projects HSE employee at building construction in the Jakarta area. These findings will serve as a guide for construction stakeholders looking to improve efficiency safety management by incorporating some LC technology into the SPC process. And four subsystems work together to create a positive circulation system that aids long-term improvement construction safety management.

Construction Safety Management of Mechanical and Electrical Installation Projects

Various industries today rely on the support of electromechanical equipment, expanding its scope of application and leading to an increase in electromechanical installation projects. However, due to the high level of expertise required and the potential risks involved, it is crucial to emphasize safety management during construction. This paper delves into the significance of construction safety management for electromechanical installation projects, identifies common problems encountered during construction, and proposes solutions. This analysis aims to provide relevant personnel with essential guidance and references for managing electromechanical installation projects safely.

Discussion on Construction Quality and Safety Management of Industrial and Civil Buildings

With the improvement of people s living standards, the improvement of the industry environment, and the development of the economy, the quality of construction projects put forward higher requirements, especially civil construction projects, in recent years its scale is constantly expanded, the development of the construction industry gradually increased impact, From how to establish the construction qual- ity management system, strengthen the building construction safety management and other aspects put forward some measures to discuss with you.

Head-Integrated Detecting Method for Workers under Complex Construction Scenarios

Real-time detection of workers is crucial in construction safety management. Deep learning-based detecting methods are valuable, but always challenged by the possibility of target missing or identity errors under complex scenarios. To address these limitations, previous research depended on re-training for new models or datasets, which are prohibitively time-consuming and incur high computing demands. However, we demonstrate that the better detecting model might not rely on more re-training of weights; instead, a training-free model can achieve even better performance by integrating head information. In this paper, a new head-detecting branch (55 MB) is added to the Keypoint Region-based Convolutional Network (Keypoint R-CNN, 226 MB) without altering its original weights, allowing for a less occluded head to aid in body detection. We also deployed motion information and anthropometric data through a post-processing module to calculate movement relationships. This study achieved an identity F1-score (IDF1) of 97.609%, recall (Rcll) of 98.173%, precision (Prcn) of 97.052%, and accuracy of 95.329% as a state-of-the-art (SOTA) method for worker detection. This exploration breaks the inertial attitudes of re-training dependency and accelerates the application of universal models, in addition to reducing the computational difficulty for most construction sites, especially in scenarios with an insufficient graphics processing unit (GPU). More importantly, this study can address occlusion challenges effectively in the worker detection field, making it of practical significance.