Site Safety Research Articles

Safety Management in Public Building Construction: A Case Study of Pokhara Valley

Background: Construction site safety is a major issue in the construction industry all around the world. In a developing country like Nepal, the number of construction industries is growing haphazardly at a high rate but they are not being able to cope with factors associated with construction site safety. Objective: The main objective of the study is to explore the status of safety management in public building construction projects and to suggest measures for improvement. Methodology: To achieve the objective of the study, data were collected from the Engineers and site management personnel through a questionnaire survey. Data were analyzed descriptively by IBM SPSS and presented in tables and diagrams. Conclusion: The identified major safety issues for public building construction sites are lack of regular safety inspection, lack of use of personal protective equipment (PPE), lack of conducting safety training, and a lack of proper safety policy and plan including a lack of an adequate budget for safety management. Increased worker safety awareness and introducing safe working practices through training and induction programs are vital to reducing construction site safety. The outcome of the study will be helpful while formulating a project-based construction site safety policy.

CBRN Disaster Hospital Response and Preparedness. An Italian Civilian Military Cooperation Teaching Model

Introduction:Chemical, Biological, Radiological, and Nuclear (CBRN) emergencies need specific hospital preparedness and resource availability.Preparing to evaluate and manage victims from CBRN exposure events in one aspect of hospital preparedness, is often underestimated. Specific skills and capabilities are required to manage these events. Emergency department (ED) and hospital staff need adequate training to provide safe and effective care.Method:The Hospital Complex of Valtellina and Alto Lario (with three hospitals), in consideration of the geographic location amid mountains, far from urban hospital centers, decided to develop an intensive training program for the hospital emergency staff. Firstly, it was based on an eight-hour initial training program, using a combined civilian military approach that included hazard recognition, substance identification, site safety, response roles, PPE use, and decontamination procedures. The CBRN Operational Unit (for prevention of chemical-biological-radiological-nuclear risks) of the 1st Territorial Unit of the Auxiliary Military Corps of the Order of Malta Italy led such training session showing that a military approach to CBRN threats can be used with civilian and military competencies and tools in managing specific hazardous events hospital first responders may face.Results:A drill exercise was performed on a radiological fall-out incident to test the hospital’s emergency response staff preparedness. Hospital management and decontamination procedures were analyzed to treat victims as well as first responders and to train hospital staff with few resources available.Conclusion:Hospitals need a specific level of preparedness to enable an effective response to CBRN emergencies. Skills and competencies of military personnel can be a resource in these kinds of events to train civilian personnel who are not normally acquainted to or confident to manage this kind of CBRN events, and can represent a new model and challenge of interagency cooperation in the disaster management of complex emergencies involving hazardous materials.

Development of a Logistics System for Disaster Medical Container Operations Using Drones and GIS

Introduction:To quickly operate a temporary hospital using disaster medical containers in the event of a large-scale disaster, dozens of large trailers must be quickly brought to the disaster site. This study aims to establish a logistic system for transporting and installing containers using drones and GIS.Method:By linking drones and GIS, a theoretical model was created to quickly determine routes and installation sites for large trailers transporting medical containers from candidate routes simulated in advance by taking into account hazard maps and road conditions.Results:Using this system, it is possible to(1) Establish whether the roads planned to be passed through are passable.By narrowing down candidate routes through preliminary simulations that overlay hazard maps and roads that are passable for large trailers, it is possible to narrow down the routes that need to be confirmed. Immediately after a disaster, a drone can be used to confirm road damage, flooding, landslides, etc., and map them on the GIS to determine the extent to which they are passable.(2) Identify locations that take into account demand and safety.Locations that are close to the disaster area, safe, and in demand for medical care can be determined and identified based on the population mesh and the damage situation.Conclusion:GIS and drones have a high affinity and are used in various ways. However, there is still no linkage between GIS and drones adapted to medical care and logistics at disaster sites in Japan. This system is indispensable to comprehensively determine the safety and demand of roads and installation sites.

Harnessing BIM with risk assessment for generating automated safety schedule and developing application for safety training

The construction industry accounts for 24.20% of all accidents in India, resulting in almost 75 accidents daily. Every construction job involves certain risk levels, often resulting in losses such as fatalities, compensation costs, man-hour loss, work delays, and ergonomic disorders in workers. Poor site safety management and hesitation towards adopting new methods and technologies often result in inefficient workflow, accidents, and inferior productivity. All these factors pile up, causing a surge in indirect costs, almost four times direct costs. The Indian construction industry employs 51 million workers but has only 4% formally trained workforce. Safety planning is currently considered a 'secondary' task due to insufficient safety budget, lack of well-defined training modules and modern tools, unawareness about underlying hazards, etc. The current study is an attempt to identify and assess underlying activity-based hazards using the Failure Mode and Effect Analysis (FMEA) approach by harnessing the potential of digital tools such as Building Information Modelling (BIM) and Visual Scripting in Dynamo v.2.10 to facilitate automation in generating 'Safety Schedule' in coordination with the project schedule. This study presents a novel idea of proactive safety planning, enabling workers and managers to foresee possible hazards associated with ongoing activity. Further, a worker training tracker prototype is developed in Microsoft Visual Studio using C# to store and track workers' safety training information. Managers can use this tracker to review workers' competence and ensure that they would have received appropriate role-based safety training. The applicability and effectiveness of the proposed method are demonstrated through a case study project.

Small and overlapping worker detection at construction sites

Although there has been study on worker detection using computer vision (CV) for the safety of construction sites, it is still challenging to identify employees who are obstructed or have poor vision. To solve these problems, we propose a method of small and overlapping target (worker) detection at a complex construction site named SOC-YOLO. The method is based on YOLOv5 and utilizes distance intersection over union (DIoU) non-maximum suppression (NMS), incorporating weighted triplet attention, expansion feature-level, and Soft-pool. Workers can be captured with overlap, particularly in large-scale construction sites, using the DIoU-based loss function, and NMS contributed to accuracy improvement. Next, we propose a weighted-triplet attention mechanism that can extract feature information from space more effectively and channel attention when learning object detection networks, using a simple average approach based on the same weight between the existing triplet attention. Next, we propose a model that adds additional predictive heads and residual connections to address the poor detection accuracy of workers photographed over long distances. A low-level feature map containing more information regarding small targets is used by extending the feature level. Finally, Softpool-spatial pyramid pooling fast (Softpool-SPPF) is proposed to solve the problem of inconsistent input image sizes. Softpool-SPPF performs an spatial pyramid pooling (SPP) function while preserving more functional information for accurate small target detection. Experiments were conducted using published worker detection datasets and handmade datasets, and the results showed increase from 81.26% to 84.63% average precision (AP) for small objects, from 67.52% to 73.88% mAP for minute objects, from 74.56% to77.57% for overlapping objects. The proposed method is expected to be useful for safety monitoring by applying it to the construction site worker tracking model.

Improved Discriminative Object Localization Algorithm for Safety Management of Indoor Construction.

Object localization is a sub-field of computer vision-based object recognition technology that identifies object classes and locations. Studies on safety management are still in their infancy, particularly those aimed at lowering occupational fatalities and accidents at indoor construction sites. In comparison to manual procedures, this study suggests an improved discriminative object localization (IDOL) algorithm to aid safety managers with visualization to improve indoor construction site safety management. The IDOL algorithm employs Grad-CAM visualization images from the EfficientNet-B7 classification network to automatically identify internal characteristics pertinent to the set of classes evaluated by the network model without the need for further annotation. To evaluate the performance of the presented algorithm in the study, localization accuracy in 2D coordinates and localization error in 3D coordinates of the IDOL algorithm and YOLOv5 object detection model, a leading object detection method in the current research area, are compared. The comparison findings demonstrate that the IDOL algorithm provides a higher localization accuracy with more precise coordinates than the YOLOv5 model over both 2D images and 3D point cloud coordinates. The results of the study indicate that the IDOL algorithm achieved improved localization performance over the existing YOLOv5 object detection model and, thus, is able to assist with visualization of indoor construction sites in order to enhance safety management.

Virtual reality enhanced multi-role collaboration in crane-lift training for modular construction

Crane lifts for bulky module installation greatly affect site safety and efficiency. However, few previous studies addressed collaborative training for a lifting crew in modular construction. Therefore, this paper aims to develop a virtual reality (VR) system for enhancing multi-role collaboration in crane-lift training. The system is developed by integrating full-, semi-, and non-immersive VR devices with role-specific functions designed for crane operators, riggers, and signalers in modular building projects. The system is objectively assessed by six groups of researchers with memory tests and proved more effective in group-based crane-lift training than the lecture-based approach, resulting in a 33% improvement in the trainees' memory stability. The system is also evaluated by the researchers and six industry practitioners who affirm that their understanding of the crane-lift process is enhanced through multi-role collaboration using the system. The developed multi-user VR system should facilitate safe and efficient delivery of modular buildings.

An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping

Landslides are one of the most destructive and common geological disasters in the Tonglvshan mining area, which seriously threatens the safety of surrounding residents and the Tonglvshan ancient copper mine site. Therefore, to effectively reduce the landslide risk and protect the safety of the Tonglvshan ancient copper mine site, it is necessary to carry out a systematic assessment of the landslide susceptibility in the study area. Combining the unascertained measure (UM) theory, the dynamic comprehensive weighting (DCW) method based on the fuzzy analytic hierarchy process (AHP)-entropy weight method and the set pair analysis (SPA) theory, an improved UM-SPA coupling model for landslide susceptibility assessment is proposed in this study. First, a hierarchical evaluation index system including 10 landslide conditioning factors is constructed. Then, the dynamic comprehensive weighting method based on the fuzzy AHP-entropy weight method is used to assign independent comprehensive weights to each evaluation unit. Finally, we optimize the credible degree recognition criteria of UM theory by introducing SPA theory to quantitatively determine the landslide susceptibility level. The results show that the improved UM-SPA model can produce landslide susceptibility zoning maps with high reliability. The whole study area is divided into five susceptibility levels. 5.8% and 10.16% of the Tonglvshan mining area are divided into extremely high susceptibility areas and high susceptibility areas, respectively. The low and extremely low susceptibility areas account for 30.87% and 34.14% of the total area of the study area, respectively. Comparison with the AHP and Entropy-FAHP models indicates that the improved UM-SPA model (AUC = 0.777) shows a better performance than the Entropy-FAHP models (AUC = 0.764) and the conventional AHP (AUC = 0.698). Therefore, these results can provide reference for emergency planning, disaster reduction and prevention decision-making in the Tonglvshan mining area.

Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.

A comprehensive evaluation method for the site selection of new healthcare facilities in geological hazard-prone areas

Healthcare facilities in geological hazard-prone areas not only are responsible for local basic medical services but also are the main provider of hazard emergency rescue work. The selection of their sites is further complicated by the need to consider both the equalization of regional medical services and resource allocation and the impact of geological hazards on site safety. Shimian County in Sichuan Province, a geological disaster-prone area, was chosen as the study area. First, suitability analysis of the construction land was used to determine the site alternatives for new healthcare facilities, and an evaluation index system of construction land suitability consisting of geological hazard susceptibility, slope and aspect was established. Then, the suitability was evaluated by the Ordered Weighted Averaging (OWA) operator, and the Analytic Hierarchy Process (AHP) and the quantitative method of Regular Increasing Monotone (RIM) were used to calculate the criterion weights and order weights in the Ordered Weighted Averaging operator respectively. The suitability results were classified into five levels: high, moderate, average, barely suitable, and unsuitable. Twelve site alternatives were identified in the highly and moderately suitable areas. Finally, a comprehensive evaluation index system consisting of indices such as construction land suitability and medical service accessibility was established, the PROMETHEE II method was conducted to comprehensively evaluate the site alternatives, and ranked results for the 12 site alternatives were obtained. These ranked results were analyzed by subindexes and Graphical Analysis for Interactive Aid (GAIA) to obtain a score for each alternative index and its similarity to the alternative, which could significantly help decision-making. This study achieves reasonable and scientific site selection for healthcare facilities in geological hazard-prone areas, and the results can provide references for relevant decision-makers.

Construction site safety and quality analysis utilizing KHNN Model

Improving construction site safety through effective hazard identification and mitigation is critical. This study aims to predict rework, defects, and associated costs using artificial neural networks and optimization algorithms. Traditional safety planning approaches lack pre-construction hazard analysis. To examine deficiencies, various metrics were analyzed, including rework costs per $1M scope and injury rates. Ineffective safety practices like inadequate training and protection have led to accidents. This work identifies approaches to enhance worker safety performance through hazard identification. Inputs to a neural network model predict rework workers, defects, and costs. Safety execution aims to systematically identify hazards before construction. Model performance using actual data was evaluated. Two soft computing methods - artificial neural network and optimization algorithms - were implemented in MATLAB. Krill herd and grey wolf optimization techniques optimized hidden neuron weights in the neural network structure. Predictions from these algorithms outperformed other existing methods like particle swarm and genetic algorithms. This study provides a framework to quantitatively forecast rework, defects, and associated costs through systematic pre-construction hazard analysis and modeling. The proposed optimizationenhanced neural network models can help construction managers implement targeted safety improvements.

A review of the emerging role of UAVs in construction site safety monitoring

Because of the inherent risks of working on a construction site, protecting construction employees' occupational and health safety (OHS) has been a pressing issue for quite long time. There needs to be a reliable method of constantly monitoring the construction site to identify and remove any possible dangers as soon as they are discovered. Unmanned Aerial Vehicles (UAVs) have been shown to be useful instruments for ensuring worker safety on construction sites, thanks to their ability to extract and interpret images and video data reliably and automatically. This paper's goal is to provide the current state of research in this area by reviewing prior attempts using bibliometric and content-based analytic techniques. The paper details many methods, with special attention paid to methods for identifying personal protective equipment (PPE). Machine learning algorithms such as SSD, YOLO, Faster-RCNN, and Efficient-Net is also discussed with an application of helmet detection. The findings highlight the most significant obstacles and limitations of the existing research, from both theoretical and applied perspectives, and point the way for future studies.

Using the Pivot Pair-Wise Relative Criteria Importance Assessment (PIPRECIA) Method to Determine the Relative Weight of the Factors Affecting Construction Site Safety Performance

Using the Pivot Pair-Wise Relative Criteria Importance Assessment (PIPRECIA) Method to Determine the Relative Weight of the Factors Affecting Construction Site Safety Performance

PPE non-compliance among construction workers: An assessment of contributing factors utilizing fuzzy theory

Introduction: Construction practitioners are at a disproportionately higher risk of fatal and nonfatal injuries compared to practitioners from other industries. The absence of and inappropriate use of personal protective equipment (PPE), hereinafter referred to as PPE non-compliance, are major causes of fatal and nonfatal injuries at construction workplaces. Method: Accordingly, a robust 4-step research methodology was employed to investigate and assess factors that contribute to PPE non-compliance. As a result, 16 factors were identified utilizing literature review and ranked utilizing fuzzy set theory and K-means clustering. Top among them: inadequate safety supervision, poor risk perception, lack of climate adaptation, lack of safety training, and lack of management support. Results: Managing construction safety in a proactive manner is vital to eliminate or minimize construction hazards and improve overall site safety. Thus, proactive measures to address these 16 factors were identified utilizing a focus group methodology. The validation of the statistical findings with that of the focus groups of industry professionals provides validation of the findings as both practical and actionable. Practical Applications: This study significantly contributes to construction safety knowledge and practice which, in turn, aids academic researchers and construction practitioners in their continuous efforts to reduce fatal and nonfatal injuries among construction workers.

Real-time site safety risk assessment and intervention method using the RFID-based multi-sensor intelligent system.

One of the main problems that may put people's safety in danger is the lack of real-time detection, evaluation, and recognition of predictable safety risks. Current real-time risk identification solutions are limited to proximity sensing, which lack providing the exposed person with risk-specific information in real-time. Combined values of concurrently presented risks are either unrecognized or underestimated. This study goes beyond the proximity sensing state-of-the-art by envisioning, planning, designing, developing, assembling, and examining an automated intelligent real-time risk (AIR) assessment system. A holistic safety assessment approach is followed to include identification, prioritization, detection, evaluation, and control at risk exposure time. Multi-sensor technologies based on RFID are integrated with a risk assessment intelligent system. System prototype is developed and examined to prove the concept for on-foot building construction workers. The evaluation of AIR assessment system's performance proved its validity, significance, simplicity, representation, accuracy, precision, and timeliness. The reliability of providing quantitative proximity values of risk can be limited due to the signal attenuation; however, it can be reliable in providing risk proximity in a subjective linguistic fashion (Near/Far). The main contributions of the AIR assessment system are that the mobile wearable device can provide a linguistic meaningful risk assessment resultant value, the value represents the combined evaluation of concurrently presented risks, and can be sound delivered to the exposed person in real-time of exposure. Therefore, AIR system can be used as an effective prognostic risk assessment tool that can empower workers with real-time recognition and measurability of risk exposure.

Use of unmanned aerial vehicle for safety inspection at construction sites: a systematic literature review

The use of drone can be a viable way of detecting and correcting errors in a timely manner, thus avoiding accidents on the construction sites. The Unmanned Aerial Vehicle (UAV) can reduce the time spent in carrying out monitoring tasks, improve the overall performance of the project and streamline the reaction process to potential risks. The main objective of this systematic literature review (SLR) is to analyze the use of unmanned aerial vehicle (UAV) for safety inspection at construction sites. The articles were searched in 3 databases, restricting the publications of scientific articles in the field of engineering in the last 10 years, in Portuguese, English and Spanish, resulting in 29 articles. From the SLR, it was possible to identify the advantages of using drones to improve activities related to the safety of construction sites, as well as to identify dangerous situations, identify the ideal use techniques and identify barriers to the use of drones for inspection of construction sites. The improvement in safety performance arising from the use of UAVs reduces accidents at construction sites, and, therefore, reduces injuries and disabilities generated by such accidents.

Application of BIM in construction site safety: Systematic review

Construction safety has become and establishment ever since decades. Construction industry especially workers involve in many activities that may expose them to serious hazards. However, with the transformation of current development in technology, safety has become vital especially the needs in improvement of its application on site via digitalization. The exploration on the needs of safety especially in Building Information Modelling has become crucial and there is a need for the application especially the digital twin. The construction industry needs to improve the safety performance by new technology. Building Information Modeling is one of the technologies that are being explored recently. Hence, this paper aims to obtain a comprehensive review especially on the current trend of analysis based on current practice of emerging technology that applied in current practice. Methodology of this paper by info analysis and VOS Viewer. There were 11 different terms were used to obtain the relevant literature including Scopus, Google and books guideline. After data cleaning, there’s almost 371 journal articles reviewed in between Jan 2011 and Aug 2021. There are 4 steps of framework being proposed which are analysis on the source of title, researcher, co-occurrence keyword and discussion. Hence from the results of co-occurrence based on the results shows that 21 keywords, as for the researcher were top 10 highlighted. It can be concluded that Building Information Modeling (BIM) and safety construction has the most citation for the current trend of research. Besides it is expected that this study has the potential outcome of research as references for next study on BIM and safety.

Enhancing the Construction Site Safety Knowledge and Awareness Amongst Civil Engineering Undergraduate Students

Enhancing the Construction Site Safety Knowledge and Awareness Amongst Civil Engineering Undergraduate Students

Construction Site Hazards Identification Using Deep Learning and Computer Vision

Workers on construction sites face numerous health and safety risks. Authorities have made numerous attempts to enhance safety management; yet incidents continue to occur, impacting both worker health and the project’s forward momentum. To that end, developing strategies to improve construction site safety management is crucial. The goal of this project is to employ computer vision and deep learning methods to create a model that can recognize construction workers, their PPE and the surrounding heavy equipment from CCTV footage. Then, the hazards can be discovered and identified based on an analysis of the imagery data and other criteria including weather conditions, and the on-site safety officer can be contacted. Our own dataset was used to train the You Only Look Once model, version 5 (YOLO-v5), which was put to use as an object detection model. The detection model’s performance in tests showed promise for fast and accurate object recognition in the field.

Taking a new view for researching occupational safety in construction: site safety practice

The “New View” of occupational safety is gaining increased attention within both the construction industry and its associated academe. With the potential to overcome the current plateau in accident rates and support the further enhancement of occupational safety on sites, the “New View” offers an alternative approach to more traditional command driven safety management and instead takes a sociotechnical perspective, valorising the workers and acknowledging their contributions to the system in the form of adaptability and resilience. Yet empirical research of “New View” thinking and practice within construction is lacking. Meaningful research in this space demands non-positivistic approaches able to reveal nuanced and local insights able to inform and illuminate “New View” practices and the contexts in which they could potentially be implemented on sites. Here, we make a methodological contribution with the aim to advance empirical research in this space. Social practice theory is employed and evaluated as an approach able to make such a useful contribution. Through the exploration and explication of the block of “site safety practice,” we demonstrate the utility of this theoretical approach for “New View” researchers, whilst also making a fundamental contribution to knowledge in the form of insights of the local and situated contexts, in which “New View” thinking could be practically applied.