Advanced system engineering approaches to dynamic modelling of human factors and system safety in sociotechnical systems

Sociotechnical systems (STSs) indicate complex operational processes composed of interactive and dependent social elements, organizational and human activities. This research work seeks to fill some important knowledge gaps in system safety performance and human factors analysis using in STSs. First, an in-depth critical analysis is conducted to explore state-of-the-art findings, needs, gaps, key challenges, and research opportunities in human reliability and factors analysis (HR&FA). Accordingly, a risk model is developed to capture the dynamic nature of different systems failures and integrated them into system safety barriers under uncertainty as per Safety-I paradigm. This is followed by proposing a novel dynamic human-factor risk model tailored for assessing system safety in STSs based on Safety-II concepts. This work is extended to further explore system safety using Performance Shaping Factors (PSFs) by proposing a systematic approach to identify PSFs and quantify their importance level and influence on the performance of sociotechnical systems’ functions. Finally, a systematic review is conducted to provide a holistic profile of HR&FA in complex STSs with a deep focus on revealing the contribution of artificial intelligence and expert systems over HR&FA in complex systems. The findings reveal that proposed models can effectively address critical challenges associated with system safety and human factors quantification. It also trues about uncertainty characterization using the proposed models. Furthermore, the proposed advanced probabilistic model can better model evolving dependencies among system safety performance factors. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. This research work would help better understand, analyze, and improve the system safety and human factors performance in complex sociotechnical systems

An advanced framework for leakage risk assessment of hydrogen refueling stations using interval-valued spherical fuzzy sets (IV-SFS)

The extensive population growth calls for substantial studies on sustainable development in urban areas. Thus, it is vital for cities to be resilient to new situations and adequately manage the changes. Investing in renewable and green energy, including high-tech hydrogen infrastructure, is crucial for sustainable economic progress and for preserving environmental quality. However, implementing new technology needs an effective and efficient risk assessment investigation to minimize the risk to an acceptable level or ALARP (As low as reasonably practicable). The present study proposes an advanced decision-making framework to manage the risk of hydrogen refueling station leakage by adopting the Bow-tie analysis and Interval-Value Spherical Fuzzy Sets to properly deal with the subjectivity of the risk assessment process. The outcomes of the case study illustrate the causality of hydrogen refueling stations' undesired events and enhance the decision-maker's thoughts about risk management under uncertainty. According to the findings, jet fire is a more likely accident in the case of liquid hydrogen leakage. Furthermore, equipment failure has been recognized as the most likely cause of hydrogen leakage. Thus, in order to maintain the reliability of liquid hydrogen refueling stations, it is crucial that decision-makers develop a trustworthy safety management system that integrates a variety of risk mitigation measures including asset management strategies

Comparing the Analgesic Effect of Aminophylline and Hyoscine with Morphine on Renal Colic: a Randomized Clinical Trial

Introduction: Although narcotics are effective for pain relief in these patients, they have little impact on the underlying cause. Therefore, surveys have been conducted to find more effective agents. Objective: This study conducted to compare the analgesic effect of aminophylline and hyoscine combination with morphine on renal colic patients. Methods: This double-blind clinical trial was conducted on patients with renal colic caused by urinary tract stones. Subjects were selected via convenience sampling method. Patients were randomly divided into two groups based on whether they received aminophylline + hyoscine or morphine. Before drug administration, one researcher was asked to measure the pain of the patients using Graduated Numbered Visual Analogue Scale (GN-VAS). Afterward, 20 mg of hyoscine along with 3 mg/kg of aminophylline in 100 cc normal saline was injected during 10 minutes into patients in the one group, whereas 0.1 mg/kg of morphine was intravenously with 100 cc normal saline to align two groups, administered to the subjects in another group. Half an hour after the administration of drugs, pain was measured for the second time. Vital signs and side effects were all recorded. Results: In this study, 95 patients (47 patients in the aminophylline+hyoscine group and 48 patients in the morphine group) remained in the trial until the end. The difference in sex distribution(p=0.227) and age(p=0.680) of the two groups was not statistically significant. Median of pain intensity was not significantly different between the two study groups (p<0.05), neither before nor after administration of the drugs. The mean time required for pain relief in morphine group was significantly lower than aminophylline+hyoscine group (5.9±1.6 vs. 11.1±1.6 minutes; p<0.001). Conclusion: Overall, our findings indicated that aminophylline + hyoscine combination was effective in reducing renal colic pain and there is no significant difference between this combination and morphine in terms of pain relief

Comparing the Analgesic Effect of Aminophylline and Hyoscine with Morphine on Renal Colic: a Randomized Clinical Trial

Introduction: Although narcotics are effective for pain relief in these patients, they have little impact on the underlying cause. Therefore, surveys have been conducted to find more effective agents. Objective: This study conducted to compare the analgesic effect of aminophylline and hyoscine combination with morphine on renal colic patients. Methods: This double-blind clinical trial was conducted on patients with renal colic caused by urinary tract stones. Subjects were selected via convenience sampling method. Patients were randomly divided into two groups based on whether they received aminophylline + hyoscine or morphine. Before drug administration, one researcher was asked to measure the pain of the patients using Graduated Numbered Visual Analogue Scale (GN-VAS). Afterward, 20 mg of hyoscine along with 3 mg/kg of aminophylline in 100 cc normal saline was injected during 10 minutes into patients in the one group, whereas 0.1 mg/kg of morphine was intravenously with 100 cc normal saline to align two groups, administered to the subjects in another group. Half an hour after the administration of drugs, pain was measured for the second time. Vital signs and side effects were all recorded. Results: In this study, 95 patients (47 patients in the aminophylline+hyoscine group and 48 patients in the morphine group) remained in the trial until the end. The difference in sex distribution(p=0.227) and age(p=0.680) of the two groups was not statistically significant. Median of pain intensity was not significantly different between the two study groups (p<0.05), neither before nor after administration of the drugs. The mean time required for pain relief in morphine group was significantly lower than aminophylline+hyoscine group (5.9±1.6 vs. 11.1±1.6 minutes; p<0.001). Conclusion: Overall, our findings indicated that aminophylline + hyoscine combination was effective in reducing renal colic pain and there is no significant difference between this combination and morphine in terms of pain relief

Analysis of Root Causes of Major Process Accident in Town Border Stations (TBS) using Functional Hazard Analysis (FuHA) and Bow tie Methods

Background and objective: To control and prevention of accidents, attention to root causes of accident occurrence is very important. Safety risk of process units located in metropolis, must be always under control and in accordance with risk acceptance criteria of the community. In this regard, the purpose of this study is identification of functional failures, root cause analysis and incident outcomes arising from gas release in the Town Border Stations (TBS). Materials and method: Using at the same time of both methods the Functional Hazard Analysis (FuHA) and Failure Mode & Effect Analysis (FMEA), identification of the failure locations and qualitative risk analysis were carried out. For identification and analysis of the causes accident, Bow tie analysis method was also used. Results: Occurrence probability of identified top events was 0.71 and its failure rate was 1.24 per year. Unsafe behavioral (FP=0.36, &lambda=0.446) and mechanical causes (FP= 0.133, &lambda= 0.142) had highest and lowest the contribution in the top event occurrence. Vapor cloud explosion (VCE) had the highest probability (0.261) and failure rate (0.243) among the all identified incidents outcomes. Conclusion: Prevention of the root causes and attention to the human factors have the considerable contribution in accident control in the process units. In the combined approach used in present study, if are considered to be, the barriers role against of the root causes and incident outcomes occurrence, it could be an appropriate approach for identification of root causes and control of the hazards process

Assessing the Reliability of the City Gate Station Using Monte Carlo Simulation

Introduction: Reliability is always of particular importance in system design and planning; thus, improving reliability is among the approaches for achieving a safe system. Simulation methods are widely used in system reliability assessment. Therefore, this study aims to assess the reliability of the City Gate Gas Station (CGS) using Monte Carlo Simulation (MCS). Material and Methods: This descriptive and analytical study was conducted in one of the CGSs of North Khorasan Province in 2021. The CGS process was carefully examined and its block diagram was plotted. Then, failure time data of CGS equipment were collected over 11 years and time between failures of subsystems was calculated. The failure probability distribution function of subsystems was determined using Easy Fit software and Kolmogorov-Smirnov test. Moreover, subsystems’ reliability was estimated by MCS. Finally, station reliability was calculated considering the series-parallel structure of the CGS. Results: The results revealed that the failure probability density distribution function of CGS subsystems was based on gamma and normal functions. The reliabilities of filtration, heater, pressure reduction system, and odorize were calculated as 0.97, 0.987, 0.98, and 0.992 respectively, and their failure rates were 0.000003477, 0.0000014937, 0.0000023062, and 0.0000009169 failures per hour respectively. The station reliability was calculated as 0.93. Conclusion: The failure probability distribution function and reliability assessment of subsystems were determined by data modeling and MCS respectively. Filtration and pressure reduction systems had the highest failure rate and required a proper maintenance program

Nash Equilibrium-Based FMEA for Risk Prioritization in Hydrogen Refueling Station Design

This chapter focuses on developing a modified Failure Mode and Effect Analysis (FMEA) method for prioritising potential failure modes in the design stage of hydrogen refuelling stations, explicitly targeting the liquid hydrogen storage (Dewar) system. The conventional FMEA method is widely used but has limitations, such as equal importance weights for risk factors, limited consideration of risk factors, and static risk assessment. To address these shortcomings, a new approach based on the “Nash equilibrium” concept is proposed. The proposed method incorporates the perspectives of multiple decision-makers (DMs) who assess the risk factors associated with each failure mode using Pythagorean fuzzy uncertain linguistic variables. Payoff matrices are constructed based on the assessments provided by the DMs, representing a zero-sum game between failure and success. The FMEA process is then formulated as a combination of game theory and risk assessment, aiming to prioritise failure modes for intervention actions. To demonstrate the application of the proposed method, a case study of the liquid hydrogen storage (Dewar) system in a hydrogen refuelling station is presented. Failure modes are identified, and their severity, occurrence, and detection are assessed. The risk priority number (RPN) is calculated for each failure mode based on the proposed method, enabling the prioritisation of failure modes for appropriate intervention actions. The proposed FMEA method offers several advantages over the conventional approach. It allows for unequal importance weights of risk factors, incorporates a comprehensive set of risk factors, avoids the issue of different combinations producing the same RPN, and provides a systematic framework for considering the dynamic nature of risk assessment over time. By integrating game theory principles and FMEA, this study offers a novel perspective on risk prioritisation in the design stage of hydrogen refuelling stations. The proposed method provides a more comprehensive and flexible approach to assessing and prioritising failure modes, contributing to developing safer and more reliable hydrogen infrastructure

Expert Judgment and Uncertainty in Sociotechnical Systems Analysis

The chapter navigates the complicated landscape of incorporating expert insights within the framework of sociotechnical systems. It talks about the pivotal role of expert judgment in unravelling the complexities inherent in these systems and addresses the challenges posed by uncertainties. It also provides an overview of the multifaceted methodologies employed in integrating expert knowledge, encompassing structured expert elicitation techniques, uncertainty quantification models, and the collaborative dynamics of expert panels. Furthermore, it explores the mathematical foundations of Bayesian approaches and fuzzy logic applications, elucidating how these methodologies contribute to a probabilistic assessment and representation of uncertainties. The chapter underscores the importance of addressing challenges such as identifying sources of uncertainty, ethical considerations, and the need for continuous learning in sociotechnical systems analysis. Through a comprehensive exploration of methodologies, real-world applications, and concerns, this chapter aims to contribute to the evolving landscape of expertise in sociotechnical systems, offering insights and implications for both research and practical applications