Safety Of Construction Workers Research Articles

Tunnel construction worker safety state prediction and management system based on AHP and anomaly detection algorithm model

Tunnels represent complex, high-risk, and technically demanding underground construction projects. The safety of construction workers in tunnels is influenced by various factors, including physiological indicators, tunnel dimensions, and internal environmental conditions. Analyzing safety based solely on static factors is inadequate for modern tunnel engineering safety management requirements. To address this challenge, this paper provides a comprehensive analysis of factors impacting safety and employs the Analytic Hierarchy Process (AHP) to identify seven significant factors with high importance: body temperature, heart rate, internal temperature, internal humidity, CO concentration, chlorine concentration, and the relative positioning of personnel. Considering these factors essential for assessing worker safety, we introduce a novel model named Tunnel-APH-AD. For training models aimed at anomaly detection, we performed data augmentation and utilized four distinct machine learning models. Additionally, ensemble learning techniques were applied to aggregate the predictions from individual models, thereby enhancing the effectiveness of detecting safety states for tunnel workers. We also evaluated the performance of these models on out-of-distribution (OOD) samples to test their robustness and generalizability. The experimental results indicate that, under similar ventilation and tunnel conditions, the ensemble learning model exhibits superior overall performance compared to individual models, underscoring the effectiveness of model combination in improving the accuracy and reliability of safety alerts. Through experimental validation, this study provides interpretable, scalable, and scientifically generalized applications of machine learning theories in systems for tunnel construction worker safety alerts. These findings contribute to advancing safety management practices in tunnel engineering, enabling proactive and effective measures to mitigate potential risks and ensure the well-being of workers.

The Cross-Cutting Effects of Age Expectation and Safety Value on Construction Worker Safety Behavior: A Multidimensional Analysis

This research investigates how age expectation (AE) influences construction worker safety behavior (CWSB) by utilizing self-validation theory (SVT). Using structural equation modeling (SEM) and data from 391 Chinese construction workers, we found that components of AE, such as physical health expectation (PHE), mental health expectation (MHE), and cognitive function expectation (CFE), positively impact CWSB. Safety value (SV), including safety risk perception (SRP) and safety attitude (SA), mediates these effects. The study provides theoretical insights and practical guidance for enhancing CWSB through interventions on AE, supporting sustainable growth and workforce quality in the construction industry.

A Bayesian network model integrating organizational, individual and psychological factors for strengthening construction worker safety behavior

Objectives. Construction worker safety behavior (CWSB) may be affected by a confluence of multilevel and interrelated factors. Cultivating and maintaining CWSB is vital for improving construction safety. Current studies focus on organization-level or individual-level CWSB antecedents. However, few studies have examined the influence of psychological factors on CWSB, thereby reducing the joint effects of multilevel factors on CWSB. Methods. To determine effective strategies for strengthening CWSB, this study adopted the Bayesian network technique to explore the interrelationships between CWSB and its antecedent factors. A Bayesian belief network model was developed and trained with data collected from Chinese construction workers, which connected organizational, individual and psychological factors with CWSB. Results. According to the sensitivity analysis, safety knowledge, safety climate and psychological capital are the three most significant influencing factors for CWSB. A combined strategy that enhances safety knowledge, safety climate and communication competence simultaneously is the most effective option for strengthening CWSB. The validation and robustness of the network showed good accuracy for safety behavior judgment. Conclusion. This study proposes an alternative way to improve safety behavior by identifying its interactive causes and illustrates the importance of initiating systematic safety measures, which may help to mitigate the problem of safety plateau.

Ghanaian Construction Workers’ Health and Safety Knowledge and Compliance

Purpose: This study seeks to assess the knowledge level of local construction workers regarding health and safety measures on construction sites. Design/Methodology/Approach: A quantitative research approach was employed, using interval Likert-type scales to measure data. A purposive sampling technique was employed for the study. One hundred and fifty questionnaires were distributed, with 122 returned and deemed valid for analysis. Descriptive statistics, relative importance index, Pearson Correlation, and structural equation modelling were utilised for the study. Data analysis was conducted using Amos 28 through structural equation modelling. Findings: The results indicate that the level of knowledge in construction health and safety practices directly and indirectly affects project performance and productivity. Higher levels of knowledge were associated with higher compliance with construction health and safety practices, leading to increased project performance and productivity. Compliance with health and safety measures directly influenced project performance and productivity. Additionally, the level of knowledge had a positive indirect effect on project performance and productivity. Research Limitations: Primarily, the sample was confined to construction workers within specific regions of Ghana, which may limit the applicability of the results to other geographic areas or contexts. Additionally, using self-reported data through questionnaires could introduce bias, as participants may respond in ways they perceive as socially desirable rather than reflecting true behaviours and knowledge levels. Practical Implication: Construction firms and authorities should prioritise safety training and tight safety regulations. Knowledge and compliance boost project performance and efficiency, making them a compelling financial case for investment. Companies could integrate advanced training modules and ongoing learning opportunities to maintain safety knowledge and compliance leading to more efficient and safer construction methods. Social Implications: The study shows substantial social consequences for construction worker health and safety. It emphasises the need for improved education, regulation, and compliance to reduce workplace injuries and fatalities. Originality/value: It contributes to understanding the relationship between project performance and productivity in a complex construction environment.

Pre-Support Method for Horizontal Overlapping Jet Grouting Columns for Very-Large Cross-Section Tunnels in Dry Silty Sand Strata: A Case Study

The Shangbai Tunnel on the Daxi Passenger Dedicated Line passes through unsaturated dry silty sand strata which is prone to sand leakage and gushing after disturbance, posing a significant threat to the safety and progress of construction work. Using the Shangbai Tunnel project as a case study, we conduct laboratory experiments to determine the physical and mechanical properties of dry silty sand layers and analyze the limitations of advanced large pipe shed pre-support and small pipe grouting pre-support structures. The objective is to evaluate the feasibility and reveal the mechanism of horizontal rotary jet grouting for column formation and pre-support. The physical and mechanical properties of the consolidated body of the overlapping columns are evaluated. Three pre-reinforcement schemes using overlapping columns for tunnels crossing different subsurface profiles are proposed. Experiments are conducted to assess the physical and mechanical properties of the overlapping columns, and on-site monitoring and analysis of the tunnel arch settlement and stress changes are performed. The results indicate that the proposed method for creating overlapping columns ensures the stability of the working face, controls arch settlement, and is cost-effective. Our study summarizes the findings from the data and information for the construction of similar projects.

Analysis of the Effect of Outdoor Thermal Comfort on Construction Accidents by Subcontractor Types

The impact of climate on construction site safety varies significantly depending on subcontractor types due to the diverse nature of workplaces and work methods. This study introduces a novel approach by categorizing construction work according to subcontractor types and assessing accident risk probabilistically through the Physiologically Equivalent Temperature (PET), an outdoor thermal comfort index. Additionally, a Hidden Markov Model (HMM)-based clustering methodology was proposed to classify new groups using PET and accident probability. This study proceeded in the following sequence: (i) collection and classification of data, (ii) PET calculation, (iii) calculation of accident probability, and (iv) clustering and Pearson correlation coefficient analysis. As a result of clustering, each group was classified according to the workplace. Groups 2 and 3 demonstrated a strong positive correlation between accident probability and PET, with correlation coefficients of 0.837 and 0.772, while Group 1 exhibited a moderately positive correlation of 0.474. This study quantitatively evaluated the impact of climate on workers for each subcontractor type using PET, an outdoor thermal comfort index for construction work, and accident probability, resulting in the identification of new groups. The findings of this study may serve as novel benchmarks for safety management in construction worker safety based on PET.

Pentingnya Penggunaan Alat Pelindung Diri (APD) pada Pekerja Bangunan terhadap Keselamatan Kerja

Construction workers are often exposed to high risks of injury and illness due to potentially dangerous work environments. Therefore, the use of Personal Protective Equipment (PPE) is very important to protect them from various threats on construction sites. Literature research conducted in recent years has provided consistent evidence regarding the benefits and importance of using PPE in the construction industry. Various types of PPE, such as safety helmets, safety shoes, protective eyewear, respirators, and gloves, have been shown to be effective in reducing the risk of physical injury and exposure to hazardous materials. Literature studies also show that implementing strict policies related to the use of PPE can reduce the rate of work accidents and work-related illnesses. In addition, proper training and awareness of the importance of occupational safety have been proven to increase worker compliance with the use of PPE. Factors such as adequate availability of PPE, strict standards, a strong safety culture, supervision, and ongoing education are key elements in ensuring effective use of PPE and improving occupational safety in construction workplaces. Therefore, continued emphasis on the correct and consistent use of PPE is a must in efforts to maintain the safety and health of construction workers throughout the world.

Elevating Innovation: Unveiling the Twin Traction Method for a 50-Ton Load Capacity Elevator in Building and Construction Applications

Most commercial elevators for buildings exceeding four stories use a cable-driven traction system. Typically, a single traction machine operates by hoisting the main cable on a traction sheave, thus vertically transporting the elevator car through rotational motion of the sheave. This research introduces a groundbreaking advancement aimed at elevating loading capacity to an unprecedented 50 tons—the highest known in the world. The innovation involves the development of a twin traction system, wherein two traction machines collaborate to lift the elevator. This novel elevator system has demonstrated remarkable capabilities, showcasing the ability to transport up to 300 passengers in a single trip. The installation of this high-capacity elevator system has yielded substantial improvements in construction work efficiency and safety protocols, particularly in scenarios where cranes are traditionally used. The newly developed elevator could lift 50 tons of equipment 60 times a day, whereas the crane was limited to 8 times. The positive impact on labor is also noteworthy, with increased safety and health considerations, especially in adverse weather conditions. By eliminating the need for manual stair climbing, the well-being of the workforce is prioritized. Furthermore, the heightened productivity resulting from a significant reduction in wait times for conventional elevators is a key outcome of this transformative technology. This research not only unveils a groundbreaking twin traction system but also highlights its multifaceted features in enhancing efficiency, safety, and overall productivity in various industries.

Leveraging YouTube to aid Construction Hazard Prevention through Design (CHPtD)

Construction Hazard Prevention through Design (CHPtD) is widely recognized as a key approach for preventing injuries in construction workplaces. It prioritizes the safety of construction workers by explicitly tackling potential hazards during the design development phase. This involves proactively incorporating design features aimed at minimizing the risk of workplace injuries. Despite the theoretical promise of CHPtD, practical implementation has faced significant challenges. Notably, prior research has emphasized issues such as designers’ unfamiliarity with construction means and methods, along with their inability to anticipate safety hazards that may arise in the field due to their design choices. In response, proponents of CHPtD have advocated for educational interventions aimed at equipping designers with the essential skills needed for effective implementation of CHPtD. This investigation assessed the effectiveness of YouTube as an educational resource for enhancing hazard recognition in simulated CHPtD review sessions among civil engineering students. YouTube was expected to aid hazard recognition due to its extensive collection of videos featuring various construction operations and related safety hazards. The study utilized a randomized controlled experiment to evaluate treatment effects. The results indicate that YouTube can serve as a valuable tool in supporting CHPtD efforts. Furthermore, the benefits derived from using YouTube were more pronounced when examining specific building elements (e.g. interior upper-level drywall) compared to other elements.

The application of the game theory combination weighting-normal cloud model to the quality evaluation of surrounding rocks

The status of surrounding rocks dramatically influences the safety of construction workers, so the quality assessment of surrounding rocks has great significance. The uniaxial saturated compressive strength of rock (X1), the quality index of surrounding rock (X2), the frictional coefficient of the structural surface (X3), the joint spacing (X4), the state of groundwater(X5), and the integrity coefficient (X6) are selected as the initial evaluation index. Then, the game theory combination weighting-normal cloud model is introduced. Second, the certainty degree matrix of each index is established, and the weight coefficients of assessment indexes are determined based on the game theory combination weighting method. Finally, the quality level of surrounding rocks is judged. Compared with the traditional methods, the proposed model solves the fuzziness and randomness of different indexes, improves the reliability of the assessment process, and enhances the predictive accuracy of assessment results. In addition, it can provide a solution scheme for the evaluation indicators, which are difficult to quantify, and reduce the influence of human factors. The results obtained from the suggested model are consistent with the current specification. Its accuracy approaches 100%, and the method is feasible for the quality level assessment of surrounding rocks, providing a new technique and approach to assessing the risk level of surrounding rocks.

SAFE PERFORMANCE OF WORKS IN CONSTRUCTION AND HOUSING AND COMMUNAL SERVICES USING THE ORIGINAL SCAFFOLDING DESIGN

Our development relates to construction, namely scaffolding and scaffolding used when working at height. It is intended for fencing the work area when carrying out various types of work, for example, when performing brickwork from scaffolding. In construction, where safety and efficiency play a crucial role, the correct selection and use of scaffolding are integral to every successful project. We have proposed a patent-protected solution. It is a scaffolding railing consisting of vertical support posts hinged with horizontal crossbars, characterised by the fact that each support post is made in the form of a telescopic connection of pipes of different diameters with the possibility of extending them from each other and is fixed with the help of an existing locking device. The hinge connection of horizontal crossbars with them, except the upper row of such bars that are railing, is made in random order, for example, by attaching a hinge to the upper end of the upper tube of the smallest diameter, and is carried out using additional ring elements mounted on the corresponding telescopic connecting tubes. The inner diameter of each of these annular elements, exceeding the outer diameter of the tube on which it is mounted, is less than the outer diameter of the next subordinate telescopic tube. Thus, it can move only vertically in a telescopic connection within this and above pipes. It makes it possible to support each of the ring elements with horizontal crossbars attached to them under their weight at a certain level of height of the vertical support post, determined by the height of the underlying tube of the telescopic connection. At the same time, the lower horizontal crossbar is hinged to the ring element of the largest diameter, put on the lower tube of the telescopic connection, and rested directly on the working flooring made in the form of a sideboard. The proposed device is not technically complex to implement. Its use increases the workers’ productivity, improves the working conditions, and ensures the safety of various construction works. Keywords: safety, construction, ease of use, prototype, fencing, scaffolding.

ANALYSIS OF FACTORS INHIBITING THE IMPLEMENTATION OF K3 IN CONSTRUCTION PROJECTS

Low implementation of K3 in construction projects can cause the safety and health of construction workers to be given less attention, so that construction workers feel less safe and comfortable, which can result in a decrease in performance levels. The impact of non-optimal performance results in the project being carried out not being able to meet the quality requirements that have been set. Based on data analysis from observations on the Kapas-Glendeng 8 bridge widening construction project, Bojonegoro Regency, there are inhibiting factors in implementing the occupational health and safety management system, namely workforce competency, work experience in the construction sector, job training, ability to access information via digital media. , worker involvement in making regulations, understanding and awareness in wearing PPE, reporting procedures, data recording and documentation.

Review - Construction Safety Management using Deep Learning and Computer Vision

Abstract: SafeZone is a deep learning and computer vision model that is aimed at improving the safety of construction workers and reducing potential hazards at construction sites. Three aspects are taken into consideration while designing the model – whether workers are wearing safety gear, potential accident zones at the site, and check for the quality of materials given to the workers. This project is intended to automate the task of identifying lack of safety and potential hazards at construction sites and subsequent implementation of safety measures to eliminate the chance of accidents. A vast majority of industrial work-sites are often never automated - and instead, manually supervised with minor application of object-based sensor technology. The project will attempt to bridge this gap with the help of Convolutional Neural Networks - which allows for a streamlined, utilitarian and flexible suite of tools that help supervisors and on-site working personnel in keeping their heads on a swivel.

Enhancing the occupational safety and well-being of mining and construction workers: an application of emotional intelligence training (EIT)

The mining and construction sector in Nigeria holds a pivotal position in driving economic development; nonetheless, it encounters notable challenges, such as project failures and safety apprehensions. The impact of leadership and safety considerations encompasses a broad spectrum of outcomes for projects and the welfare of individuals. The primary aim of this study is to investigate the possibility of inducing significant changes by introducing emotional intelligence (EI) training in the mining and construction industries in Ogun State, Nigeria. The present study employed a mixed-methods research design to authenticate the significant enhancements in post-training emotional intelligence (EI) levels seen among personnel at New Creation Constructions Ltd. The study included the consideration of five distinct groups (A-E) that were responsible for managing comparable projects. The optimization of Group E’s improvement was achieved by the implementation of Emotional Intelligence (EI) training, and subsequently compared to the performance of the remaining four groups. The safety procedures within Group E experienced a substantial enhancement, marked by an escalated utilization of protective equipment, heightened consciousness of safety measures, and a reinforced dedication to safeguarding the welfare of colleagues. Group E exhibited outstanding project management efficiency, displaying commendable performance in multiple areas such as budget adherence, adherence to safety procedures, stakeholder satisfaction, effective communication, and proficient procurement management. To optimize the benefits outlined above, we suggest implementing emotional intelligence (EI) training programs, leadership development initiatives, stress management interventions, regular evaluations of well-being, and focused research tailored to the specific needs of the organization.

Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types

Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activities, and tools/materials as input features, through the application of machine learning-based multi-class classification algorithms. 152,867 construction accident summary reports, composed of both structured (construction task, construction activity, accident type) and unstructured data (tools/materials) were used for the study. The study employed several data processing techniques, including keyword extraction through text mining, Boruta feature selection, and SMOTE data resampling enhance model accuracy. Three performance metrics (Multi-class area under the receiver operating characteristic curve (MAUC), Multi-class Matthews Correlation Coefficient (MMCC), Geometric-mean (G-mean)) were used to compare the predictive performance of four machine learning algorithms, including Decision tree, Random forest, Naïve bayes, and XGBoost. Of the four algorithms, XGBoost showed the highest performance in predicting accident type (MAUC: 0.8603, MMCC: 0.3523, G-mean: 0.5009). Furthermore, a Shapley additive explanation (SHAP) analysis was conducted to visualize feature importance. The findings of this study make a valuable contribution to improving construction safety by presenting a prediction model for accident types derived from real-world big data.

Application and research of artificial intelligence in civil engineering intelligent construction

When artificial intelligence (AI) begins to intervene, the production and lifestyles of various industries have also undergone great changes. The civil engineering construction industry has taken this opportunity to carry out the industry transformation, from traditional civil engineering construction to the intelligent construction of civil engineering with the participation of AI. Through the dynamic tracking and data analysis of construction sites and buildings through AI, the life safety of construction workers can be ensured by improving efficiency and ensuring quality. This paper analyzes the feature of intelligent construction, and discusses the current situation of intelligent construction and the application progress of intelligent construction in civil engineering construction, including Building information modeling (BIM) technology, Internet of Things and big data technology, AI technology, virtual reality technology, three-dimensional scanning technology, intelligent equipment and construction robots. At the same time, the problems and disadvantages of AI in the construction field are analyzed, and the application prospect of intelligent construction technology in future engineering construction is forecasted.

Development of stochastic deep learning model for the prediction of construction noise

Construction noise is an occupational noise that is potentially harmful, and it usually originates from machinery in construction sites. The impact of construction noise on the health and safety of construction workers is one of the main concerns in the industry. Prolonged noise exposure duration will cause physiological, physical, psychological damage, and negatively affect the working performance of the workers as well. Furthermore, the adverse impacts arising from construction noise may jeopardize public welfare, particularly for those who live nearby the construction site. Therefore, this research aims to develop a Stochastic Deep Learning (SDL) noise prediction model by integrating stochastic modelling and deep learning techniques. Stochastic modelling is applied in this study to generate a set of simulated randomized data based on several parameters as the input for the deep learning model. The deep learning model is trained with the input from stochastic modelling to predict the noise levels emitted from the construction site. The predictive performance of the deep learning model will be assessed with several statistical measures such as absolute difference, mean absolute difference and root mean square deviation. Ten case studies are conducted to validate the reliability and accuracy of the SDL noise prediction model. The SDL model showed high accuracy of prediction results with an average absolute difference of less than 1.2 A-weighted decibels (dBA) among the case studies as compared to the measurement. The reliability of the results from the prediction model is high. In conclusion, the SDL model is established and provided a promising outcome with satisfactory predictive performance. Finally, further development of the model would be worthwhile to fully exploit the potential of the SDL noise prediction model in construction industries as a planning, managerial and monitoring tool.

Dynamic assessment of dust hazard risk in the reconstruction of old industrial buildings: coupling effects of dust distribution and personnel trajectories.

Since the old industrial buildings bear many functions of industrial production and storage in the service stage, the dust generated by the regeneration construction is often accompanied by industrial pollutants, causing irreversible damage to the personal safety of construction workers. However, little consideration has been given to the uncertainty of dust emissions and the dynamics of construction personnel movement. Therefore, reducing the risk of dust hazards during the regeneration of old industrial buildings is imperative. This study draws on the trace intersecting theory to analyse the cause of the hazard risk associated with reconstruction dust and explore the impact of the spatiotemporal distribution characteristics of reconstruction dust and its coupling effect with construction on-site personnel activity trajectories and uses the risk matrix method to assess the dust hazard risk in the reconstruction of old industrial buildings. Finally, the renovation and reinforcement process of the first floor of a printing building in Xi'an was considered as an example for verification. The results indicate that the risk assessment model results were highly consistent with the actual situation and risk value for the entire area was 6.05, indicating a risk level of IV. Immediate measures should be implemented to reduce dust concentrations or the frequency of construction personnel activity, thereby minimising potential harm.

Leveraging Technology in Advancing Construction Workers Safety

Construction worker safety is a top priority in the construction business. It refers to the procedures, protocols, and practises put in place to ensure the safety and physical well-being of construction workers. Because of the presence of heavy gear, high work zones, and possible exposure to different dangers such as falls, equipment accidents, toxic chemicals, and structure collapses, construction sites are inherently hazardous settings. Safety for construction workers cannot be stressed. Construction safety is a guideline that construction safety managers follow and implement. It is the consequence of proper equipment use, worker safety, frequent site inspections, and risk assessments. Compliance with regional safety and health requirements is a critical component of construction safety. In many nations, ensuring worker safety is not just a moral imperative, but also a legal necessity. Construction businesses must offer a safe working environment and follow safety regulations and guidelines to avoid accidents, injuries, and fatalities. To reduce the occurrence of construction accidents and enhance the safety record of construction projects, dangerous elements must be identified by monitoring construction workers' behaviours. The aim of this study is to look at the implementation and adoption of technologies for construction workers safety as well as the opinions and perspectives of the construction industry towards these technologies.

Multi-parameter Inversion of Tunnels Based on Deep Learning Constraints

When tunnels traverse unfavorable geological formations, they often encounter the risk of sudden water-related incidents, posing a severe threat to the safety of construction workers and property. Consequently, there is an urgent demand for conducting research on the early detection of potential water-related disasters ahead of tunnel excavation. Among the various advanced exploration techniques, the DC resistivity method has proven to be more cost-effective and highly responsive to water-bearing structures, making it a prevalent choice for addressing sudden water hazards in real tunnel projects. This study introduces an innovative electrical approach underpinned by deep learning constraints, fusing resistivity and chargeability to pinpoint the location and size of underground water bodies. More specifically, we’ve designed a learning model rooted in the traditional U-Net convolutional network architecture, featuring a single encoder for feature extraction and a dual decoder for output. When compared to conventional linear inversion techniques and standalone parameter-based deep learning inversion methods, our proposed joint inversion approach exhibits superior performance.