Safety Management Methodology Research Articles

Research on computer vision application for safety management in construction

In light of the complexity of building sites, safety management has always been a challenging and crucial duty. The goal of construction safety management is to ensure the safe completion of the project, maintain the life and health of workers, and decrease construction accidents and losses. The emergence of computer vision (CV) technology has transformed the old construction safety management methodology. This paper demonstrates how CV technology can increase safety and management efficiency at construction sites from an application standpoint. Firstly, the principles and methods of CV technology are introduced, and the literature analysis methodology used in this paper is also descripted in detail. Then, its application in various aspects such as real-time monitoring of construction sites, worker safety management, equipment behaviour monitoring and material quality control are researched. In addition, this paper demonstrates the significant potential of CV technology in reducing accidents and enhancing safety performance, and provides important insights into future research prospects. Finally, this paper presents a more detailed picture of the construction industrys aim to utilize CV technology to improve safety management, and is both informative and instructional.

Dam safety management methodology in front of the national dam safety policy

Dam safety management methodology in front of the national dam safety policy

Development of Automatic Detection and Visualization System for Stone Pagoda Damage Based on Deep Learning

Damage diagnosis through monitoring is essential for systematic and efficient conservation and management of cultural heritage. In this study, we developed a deep learning system that automatically detects and visualizes damage to stone pagodas to enable regular monitoring of cultural heritage. Mask R-CNN was used to detect and visualize damage in pixel units in stone pagoda images. A dataset specialized for stone pagodas in Korea was built and applied to train the model. The generalized performance of the trained model was evaluated on the five-story stone pagoda at Jeongnimsa Temple Site. The damage detection recall for each type was in the range of 0.86 to 0.62 based on IoU 0.50, and the damage area segmentation recall was in the range of 0.68 to 0.51. This study suggests a new safety management methodology by applying artificial intelligence to cultural heritage sites and has important applications in cultural heritage preservation.

Predictive Analysis of Airport Safety Performance: Case Study of Split Airport

A predictive safety management methodology implies steps and tools of predictive safety management in aviation, i.e., use of predictive (forecasting) and causal modeling methods to identify potential and possible hazards in the future, as well as their causal factors which can help define timely and efficient mitigation measures to prevent or restrain emerging hazards turning into adverse events. The focus of this paper is to show how predictive analysis of an organization’s safety performance can be conducted, on the sample airport. A case study regarding implementation of predictive analysis of an organization’s safety performance, was performed at Split Airport. The predictive analysis of an airport’s safety performance was conducted through the analysis of Split Airport safety database, causal modeling of Split Airport organizational and safety performance indicators, outlier root cause analysis of Split Airport safety performance indicators, predictive analysis of safety performance (forecasting of Split Airport organizational and safety performance indicators), and scenario cases that simulate future behavior of Split Airport safety performance indicators. Based on detected future hazards, and their causal factors, the appropriate mitigation measures are proposed for the purpose of improving and maintaining an acceptable level of safety at the airport.

Conceptual Model of Predictive Safety Management Methodology in Aviation

Due to the continuous growth of air traffic and the development of aviation systems, the current safety management methodologies should be improved and upgraded. Safety management systems help aviation organizations to manage, maintain and increase safety efficiently. The focus of the research is on the development of the predictive safety management methodology to upgrade current reactive and proactive safety management methodologies and to improve the overall safety level in aviation organizations. Predictive methods are used in various aviation sectors (air navigation services, airport operations, airline operations) for planning purposes but not in the segment of safety management. Available examples of predictive methods were tested and analyzed. Time series decomposition methods were selected as most suited for implementation in aviation safety management. The paper explicitly emphasizes correlations between safety management methodologies in the sample aviation organization. The paper also shows how causal links among organizational and safety performance indicators can be detected, by developing causal models of mutual influences using causal modeling methods, on the sample organization. This research defined steps and tools of the conceptual model of predictive safety management methodology, which enables an organization to identify and mitigate future adverse events.

Predictive Safety Management System Development

Safety management systems are used to systematically manage safety risks. The paper describes and explains safety management systems in the field of aviation. Three aviation safety management methodologies are presented in the paper: reactive, proactive, and predictive. The aim is to show how safety management systems operate in each of the three methodologies. The focus of the paper is on predictive safety management methodology, its advantages, and potential uses. An overview of predictive methods used in the aviation industry is also provided. The research collected information on each safety management methodology, and revealed correlations between them, improving our understanding of safety management systems in general. Based on research described in the paper, the author proposes the development of a more advanced safety management system, i.e. a predictive safety management system which would entail the development of an expanded and well-organised safety database, as well as the use of predictive (forecasting) methods to identify potential and emerging hazards, trends and behaviour patterns.

System Approach in Road Safety Studies

The road safety management methodology should be based on a system approach. This means that the road transport must be formalized as a complex system (CS), and then safety can be interpreted as an emergent feature of such a system. Road accidents should be interpreted as "organizational accidents". They should be studied using concepts such as "normal accident theory" (NAT) and "highly reliable organization" (HRO). The main purpose of the article is to show the usefulness of these concepts for the road safety and risk management, especially in Polish conditions. The system approach to road safety research (and transport safety) will allow for the better safety results.

Resilience Engineering Indicators and Safety Management: A Systematic Review.

A safe work environment is crucial in high-risk industries, such as construction refurbishment. Safety incidents caused by uncertainty and unexpected events in construction refurbishment systems are difficult to control using conventional safety management techniques. Resilience engineering (RE) is proposed as an alternative to traditional safety management approaches. It presents a successful safety management methodology designed to deal with uncertainty in high-risk work environments. Despite the fact that RE resides in the safety domain, there is no common set of RE indicators to measure and assess resilient in the work environment. The main aim of this research is to explore RE indicators that have been identified as important in developing and assessing the resilient work environment in high-risk industries, particularly in construction refurbishment. Indicators have been attained through a systematic literature review of research and scholarly articles published between the years 2004 and 2019. The literature review explored RE indicators in various industries. Descriptive analysis and co–occurrence-based network visualization were used for data analysis. The findings revealed 28 RE indicators in 11 different high-risk industries. The results show that the four commonly used indicators were: top-management commitment, awareness, learning, and flexibility, all of which have a strong relationship with RE. The findings of this study are useful for stakeholders when making decisions concerning the most important RE indicators in the context of their research or practice as this would avoid the ambiguity and disparity in the identification of RE indicators.

Risk-based underground pipeline safety management considering corrosion effect

Due to the long term usage and irregular maintenance for corrosion checks, catastrophic accidents have been increasing in underground pipelines. In this study, a new safety management methodology of underground pipeline, risk-based pipeline management, is introduced reflecting corrosion effect. First, principle of the risk-based pipeline management is presented compared with an original method, qualitative measure. It is distinguished from the qualitative measure by reflecting societal risk and corrosion in safety management of underground pipeline. And then, it is applied to an existing underground propylene pipeline in Ulsan Industrial Complex, South Korea. The consequence analysis is based on real information, and the frequency analysis reflects degree of corrosion. For calculation of corrosion rate, direct current voltage gradient (DCVG) and close interval potential survey (CIPS) are conducted. As a result of applying the risk-based pipeline management, risk integral is reduced by 56.8% compared to the qualitative measure. Finally, sensitivity analysis is conducted on variables, which affect the risk of the pipeline. This study would contribute to introduce quantitative measure to pipeline management and increase safety of pipeline.

Ontology development for run-time safety management methodology in Smart Work Environments using ambient knowledge

This paper presents the development of a decision support system for run-time safety management in Smart Work Environments (SWEs). Our approach consists of four main phases: (i) definition of the basic steps of a methodology for run-time safety management; (ii) development of an ontological knowledge-base of safety in work environments; (iii) definition of constraints on the ontology based on organizations’ safety protocols; (iv) communication of relevant information to each actor in the safety management team. We propose a generic ontological model of safety expertise, based on Occupational Safety and Health Regulations (OSHA), that is employed as Knowledge required in our safety management methodology based on the MAPE-K (Monitor–Analyze–Plan–Execute and Knowledge) loop. We present the RAMIRES (Risk-Adaptive Management in Resilient Environments with Security) tool, implementing this methodology. RAMIRES is developed as a dashboard, supporting the safety management team in understanding the risk and its consequences, and to support decision making in risk treatment. RAMIRES interacts with the SWE and the safety management team (actors) in order to: (i) communicate the risks and preventive strategies to actors; (ii) obtain more data about the observed areas to understand the risk and its consequences; and (iii) execute the automatic preventive strategies and support actors in the execution of human-operated preventive strategies. In this paper, we show the details on concepts designed in the safety ontology and illustrate how these concepts can be extended to provide an abstract model of a specific use case. Furthermore, we propose the definition of constraints on the ontology using logic-based rules. Finally, we discuss the advantages and limitations of the proposed methodology regarding the resilience of the environment.

SCAP: a new methodology for safety management based on feedback from credible accident-probabilistic fault tree analysis system

As it is conventionally done, strategies for incorporating accident — prevention measures in any hazardous chemical process industry are developed on the basis of input from risk assessment. However, the two steps — risk assessment and hazard reduction (or safety) measures — are not linked interactively in the existing methodologies. This prevents a quantitative assessment of the impacts of safety measures on risk control. We have made an attempt to develop a methodology in which risk assessment steps are interactively linked with implementation of safety measures. The resultant system tells us the extent of reduction of risk by each successive safety measure. It also tells based on sophisticated maximum credible accident analysis (MCAA) and probabilistic fault tree analysis (PFTA) whether a given unit can ever be made ‘safe’. The application of the methodology has been illustrated with a case study.

An auditing methodology for safety management of the Greek process industry

Process Risk Management Audit (PRIMA), a methodology aiming at assessing the impact on safety of the management system in a plant is outlined. The methodology has been applied in two plants in Greece within the framework of a cross-national application and results concerning both the methodology and the Greek plants are reported and discussed.