A Big-Data-based platform of workers' behavior: Observations from the field

Risk pre-control mechanism of mines based on evidence-based safety management and safety big data

The overexploitation of mineral resources and the heavy use of mineral resources have caused serious environmental damage. The growing problem of mine safety also directly threatens the personal safety of the surrounding population and hinders the development of the local economy. Evidence-based safety eliminates the reliance on intuition and unsystematic aspects of traditional safety management systems by taking into account the actual production situations on site, making safety decision-making activities more scientific. However, there is frequently a lag in the transformation and feedback of evidence information, which obstructs the realization of effective safety decision-making activities. From the perspective of process safety management risk analysis and the transformation of safety big data and safety evidence, this paper proposes a new mine risk pre-control mechanism. First and foremost, based on process safety management, evidence-based safety is successfully applied to mine risk control. Secondly, from the perspective of information transformation, a mine risk pre-control mechanism based on evidence-based safety management and safety big data is established. Finally, taking mine open area monitoring as an example, the application analysis of the mine risk pre-control mode constructed above is carried out. The risk pre-control mechanism proposed in this paper provides a new idea for the practice of mine risk management.

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.

Construction motion data library: an integrated motion dataset for on-site activity recognition

Identifying workers’ activities is crucial for ensuring the safety and productivity of the human workforce on construction sites. Many studies implement vision-based or inertial-based sensors to construct 3D human skeletons for automated postures and activity recognition. Researchers have developed enormous and heterogeneous datasets for generic motion and artificially intelligent models based on these datasets. However, the construction-related motion dataset and labels should be specifically designed, as construction workers are often exposed to awkward postures and intensive physical tasks. This study developed a small construction-related activity dataset with an in-lab experiment and implemented the datasets to manually label a large-scale construction motion data library (CML) for activity recognition. The developed CML dataset contains 225 types of activities and 146,480 samples; among them, 60 types of activities and 61,275 samples are highly related to construction activities. To verify the dataset, five widely applied deep learning algorithms were adopted to examine the dataset, and the usability, quality, and sufficiency were reported. The average accuracy of models without tunning can reach 74.62% to 83.92%.

Workers’ Unsafe Actions When Working at Heights: Detecting from Images

Working at heights causes heavy casualties among workers during construction activities. Workers’ unsafe action detection could play a vital role in strengthening the supervision of workers to avoid them falling from heights. Existing methods for managing workers’ unsafe actions commonly rely on managers’ observation, which consumes a lot of human resources and impossibly covers a whole construction site. In this research, we propose an automatic identification method for detecting workers’ unsafe actions, considering a heights working environment, based on an improved Faster Regions with CNN features (Faster R-CNN) algorithm. We designed and carried out a series of experiments involving five types of unsafe actions to examine their efficiency and accuracy. The results illustrate and verify the method’s feasibility for improving safety inspection and supervision, as well as its limitations.

Big Data Technology in Construction Safety Management: Application Status, Trend and Challenge

The construction industry is a high-risk industry with many safety accidents. The popularity of Internet information technology has led to an explosion in the amount of data obtained in various engineering fields, and it is of necessary significance to explore the current situation of the application of big data technology in construction safety management. This paper systematically reviews 66 articles closely related to the research topic and objectives, describes the current status of big data application to various construction safety issues from the perspectives of both big data collection and big data analysis for engineering and construction projects, and categorically lists the breakthrough results of big data analysis technology in improving construction safety. Finally, the trends and challenges of big data in the field of construction safety are discussed in three directions: the application of big data to worker behavior, the prospect of integrating big data technologies, and the integration of big data technologies with construction management. The aim of this paper is to demonstrate the current state of research on big data technology fueling construction safety management, providing valuable insight into improving safety at engineering construction sites and providing guidance for future research in this field.

Big Data in Construction: Current Applications and Future Opportunities

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.

Advancing safety analytics: A diagnostic framework for assessing system readiness within occupational safety and health

Big data and analytics have shown promise in predicting safety incidents and identifying preventative measures directed towards specific risk variables. However, the safety industry is lagging in big data utilization due to various obstacles, which may include lack of data readiness (e.g., disparate databases, missing data, low validity) and personnel competencies. This paper provides a primer on the application of big data to safety. We then describe a safety analytics readiness assessment framework that highlights system requirements and the challenges that safety professionals may encounter in meeting these requirements. The proposed framework suggests that safety analytics readiness depends on (a) the quality of the data available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure, including technological platforms and skills required for data collection, storage, and analysis of health and safety metrics, and (d) measurement culture, or the emergent social patterns between employees, data acquisition, and analytic processes. A safety-analytics readiness assessment can assist organizations with understanding current capabilities so measurement systems can be matured to accommodate more advanced analytics for the ultimate purpose of improving decisions that mitigate injury and incidents.

A Systematic Review of Construction 4.0 in the Context of the BIM 4.0 Premise

This paper presents a systematic review of Construction 4.0 in the context of the building information modeling (BIM) 4.0 premise. It comprises a review of the industry in the pre-fourth industrial revolution (4IR) age, the current and anticipated development of the 4IR, Construction 4.0’s origin and applications, and the synergy of its main drivers, i.e., the synergy of BIM with the internet of things (IoT) and big data (BD). The main aim of the paper is to determine the Construction 4.0 drivers and to what extent are they initialized by the 4IR, their development and their synergy with BIM, and the direction of BIM’s implementation in the construction phase. It was found that the main drivers of Construction 4.0, which originated from the 4IR, are BIM, IoT, and BD, but with specific implementations. The results of the analysis of BIM with IoT and/or BD revealed that the integrative approaches combining the aforementioned drivers show signs of project enhancement by providing significant benefits, such as improved real-time monitoring, data exchange and analysis, construction planning, and modeling. Furthermore, it was revealed that the main drivers are mostly applied in the project’s preconstruction phase, which is continuously developing and becoming more automated. The state-of-the-art review presented in this paper suggests that BIM is in transition, adopting Construction 4.0 to become BIM 4.0.

Influencing Factors, Mechanism and Prevention of Construction Workers' Unsafe Behaviors: A Systematic Literature Review

Unsafe behaviors of construction workers are one of the main causes of accidents at construction sites. The research on unsafe behaviors of workers helps to reduce the incidence of accidents and has attracted much attention. However, a systematic literature review in this field is still lacking, which hinders stakeholders' comprehensive understanding of the unsafe behaviors of construction workers. Therefore, the aim of this study is to address this research gap based on retrieved literature from the Web of Science. First, the study conducted a descriptive analysis of the year, quantity, publishing organization, and keywords of the literature. In addition, three research topics were identified and discussed, including the influencing factors of construction workers' unsafe behaviors, the formation mechanism of unsafe behaviors, and the pre-control methods of unsafe behaviors. Moreover, a research framework was proposed and future research directions were also suggested. The research findings promote stakeholders' understanding of the influencing factors, formation mechanism, and pre-control methods of construction workers' unsafe behaviors, and lead to future research directions in the studied field.

Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

Prioritizing the indicators influencing permit to work system efficiency based on an analytic network process

A permit to work (PTW) system is a formal procedure designed to control non-routine and hazardous works. However, this system by itself does not prevent incidents, and various factors contribute to its efficiency. This study aims to prioritize the indicators influencing system efficiency. To do this, indicators of the system were identified and scored by 15 safety experts. Next, priority weights of the indicators were analyzed by an analytic network process and Super Decisions version windows 2.10. Accordingly, the nine main indicators and 43 sub-indicators influencing the PTW system were ranked. The main indicators from high to low were preventive actions, training, safe procedures, emergency system, control and corrective measures, coordination, monitoring, details of the permit form and documentation, respectively. The present work helps identify the involved indicators in PTW system efficiency. Thereby, the experts can prioritize and perform measures to prevent failures in the system and decrease accidents.

Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety

A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety prediction for scaffolds. It implements a divide-and-conquer technique with deep learning. As a test scaffolding, a four-bay, three-story scaffold model was used. Analysis of the model led to 1411 unique safety cases for the model. To apply deep learning, a test simulation generated 1,540,000 datasets for pre-training, and an additional 141,100 datasets for testing purposes. The cases were then sub-divided into 18 categories based on failure modes at both global and local levels, along with a combination of member failures. Accordingly, the divide-and-conquer technique was applied to the 18 categories, each of which were pre-trained by a neural network. For the test datasets, the overall accuracy was 99%. The prediction model showed that 82.78% of the 1411 safety cases showed 100% accuracy for the test datasets, which contributed to the high accuracy. In addition, the higher values of precision, recall, and F1 score for the majority of the safety cases indicate good performance of the model, and a significant improvement compared with past research conducted on simpler cases. Specifically, the method demonstrated improved performance with respect to accuracy and the number of classifications. Thus, the results suggest that the methodology could be reliably applied for the safety assessment of scaffolding systems that are more complex than systems tested in past studies. Furthermore, the implemented methodology can easily be replicated for other classification problems.

A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018

Purpose

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.

Design/methodology/approach

This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

Findings

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

Practical implications

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.

Originality/value

The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

Improved safety checklist analysis approach using intelligent video surveillance in the construction industry: a case study

The construction industry is extremely high risk, and safety checklist analysis is a widely used approach for safety assessment. To overcome its limitations, this article proposes an improved safety checklist analysis approach using intelligent video surveillance to replace on-site inspection. Then, a case study on metro tunnel construction is adopted to illustrate the process. First, the checklist is prepared. Second, the inspection items are correlated with construction areas, and intelligent cameras are positioned to cover the major areas of the construction site to guarantee that all the items can be checked. Thus, problems with inspection items are automatically identified and recorded. Third, the inspection items are marked by a remote scoring mechanism for safety assessment. Finally, the efficiency of the improved approach is tested by a comparative analysis among three groups. The application results indicate the feasibility of the improved approach for evaluating the safety management performance at construction sites.

Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

Big data is now widely used in many fields and is also widely applied to the integration of disciplines. Traditional methods of safety psychology are not well suited for analyzing psychological states, especially in the management of human factors in industrial production. Also, big data now becomes a new way to excavate related insight by analyzing a large amount of psychological data. So, this paper is to propose the concept of big data of safety psychology (BDSP) and to illustrate the challenges of applying big data in safety psychology. First, this paper puts forward the concept of BDSP and analyzes the difference between BDSP and traditional sample data. Subsequently, this paper summarizes the classification standard and basic characteristic of BDSP, explores the framework of BDSP and then constructs a three-dimensional structure of BDSP. Lastly, this paper discusses the challenges of using BDSP. This study is of great help to safety practitioners to solve psychological issues in the safety domain, and points out one of the research trends of human factor in industrial safety.

Big Data in occupational medicine: the convergence of -omics sciences, participatory research and e-health

Background:

New occupational hazards and risks are emerging in our progressively globalized society, in which ageing, migration, wild urbanization and rapid economic growth have led to unprecedented biological, chemical and physical exposures, linked to novel technologies, products and duty cycles. A focus shift from worker health to worker/citizen and community health is crucial. One of the major revolutions of the last decades is the computerization and digitization of the work process, the so-called “work 4.0”, and of the workplace.

Objectives:

To explore the roles and implications of Big Data in the new occupational medicine settings.

Methods:

Comprehensive literature search.

Results:

Big Data are characterized by volume, variety, veracity, velocity, and value. They come both from wet-lab techniques (“molecular Big Data”) and computational infrastructures, including databases, sensors and smart devices (“computational Big Data” and “digital Big Data”).

Conclusions:

In the light of novel hazards and thanks to new analytical approaches, molecular and digital underpinnings become extremely important in occupational medicine. Computational and digital tools can enable us to uncover new relationships between exposures and work-related diseases; to monitor the public reaction to novel risk factors associated to occupational diseases; to identify exposure-related changes in disease natural history; and to evaluate preventive workplace practices and legislative measures adopted for workplace health and safety.

Evaluating Targeted Intervention on Coal Miners' Unsafe Behavior

Miners’ unsafe behavior is the main cause of roof accidents in coal mines, and behavior intervention plays a significant role in reducing the occurrence of miners’ unsafe behavior. However, traditional behavior intervention methods lack pertinence. In order to improve the intervention effect and reduce the occurrence of coal mine roof accidents more effectively, this study proposed a targeted intervention method for unsafe behavior. The process of targeted intervention node locating was constructed, and based on the analysis of 331 coal mine roof accidents in China, three kinds of targeted intervention nodes were located. The effectiveness of targeted intervention nodes was evaluated by using structural equation model (SEM) through randomly distributing questionnaires to miners of Pingdingshan coal. The results show that, in preventing roof accidents of coal mines, the targeted intervention nodes have a significant positive impact on the intervention effect. The method can also be applied to the safety management of other industries by adjusting the node location and evaluation process.

Identification of Safety-Related Opinion Leaders among Construction Workers: Evidence from Scaffolders of Metro Construction in Wuhan, China

This study aimed to reveal opinion leaders who could impact their coworkers’ safety-related performance in Chinese construction teams. Questionnaires were distributed to 586 scaffolders in Wuhan to understand their opinions about influencing their coworkers, serving as the foundation for a social network analysis to identify the potential opinion leaders among workers. A further controlled trial with the identified workers was conducted to select real opinion leaders by comparing their influence on others’ safety-related behavior, followed by an association analysis to profile these opinion leaders. Two main sources of opinion leaders were identified: foremen and seasoned workers. Implementing interventions through opinion leaders resulted in better safety-related behavior performance. Furthermore, compared with education level, the association analysis results indicated that one’s practical skills and familiarity with respondents was more important in the formulation of opinion leaders. This research introduces the concept of opinion leaders into construction safety and proposes an approach to identify and validate opinion leaders within a crew, thus providing a tool to improve behavior promotion on sites, as well as a new perspective for viewing interactions among workers.

An evaluation of the effectiveness of the Behaviour Based Safety Initiative card system at a cement manufacturing company in Zimbabwe

Background

A behavior-based safety initiative card-issuing system was introduced at a cement-manufacturing company in Zimbabwe in 2008 to try and curb accident occurrence. The purpose of this study was to evaluate the effectiveness of the Behaviour Based Safety Initiative card system as a tool used for reducing accident frequencies.

Methods

A mixed-method approach that involving administering piloted questionnaires to 40 out of 244 randomly selected employees, making observations, and reviewing secondary data were done to collect data from different sources in the organization in 2013. A paired t-test was conducted to test whether there was significant difference in accident occurrence before and after the implementation of the BBSI. Scatterplots were also used to establish the correlation between the issuance of cards and the accident and injury occurrence.

Results

The findings suggest that the introduction of the card system brought a significant decrease in accident and injury occurrence. A negative correlation between card issuance and accident occurrence was observed, i.e., the greater the number of cards issued, the fewer the number of accidents. It was also noted that the card system positively influenced the mindset of workers towards safe work practices.

Conclusion

The card system had an influence on the reduction of accidents and injuries. The organization should leverage on issuing more cards to further reduce the number of accidents and injuries to zero.