Prediction of human error probability during the hydrocarbon road tanker loading operation using a hybrid technique of fuzzy sets, Bayesian network and CREAM

Human error identification and risk assessment in loading and unloading of petroleum products by road trucks using the SHERPA and fuzzy inference system method

Human error constitutes one of the primary causes of accidents, particularly in the context of loading and unloading operations involving road trucks, especially those carrying petroleum products. The process of identifying and evaluating human errors within these operations involves several key steps. Initially, all sub-tasks associated with loading and unloading are meticulously identified and analyzed utilizing Hierarchical Task Analysis (HTA), achieved through direct observation, document examination, and interviews. Subsequently, potential human error modes within each task are delineated using the Systematic Human Error Reduction and Prediction Approach (SHERPA). Finally, essential data for determining the criticality, probability, and severity of each error are gathered through expert elicitation and the application of Fuzzy Inference Systems (FIS). Through the analysis of SHERPA worksheets, a total of 37 errors during loading operations and 14 errors during unloading operations of petroleum products were identified. Among these errors, the predominant category during loading operations was action errors, comprising 31 instances, while communication errors were the least frequent, occurring only twice. Similarly, action errors were most prevalent during unloading operations, constituting 13 instances. These errors were further categorized and ranked based on their risk levels, resulting in 27 levels for loading operations and 12 levels for unloading operations. The consistent occurrence of action errors underscores the need for implementing control measures to mitigate their frequency and severity. Such strategies may include periodic training sessions to reinforce proper work procedures and the development of monitoring checklists, among other interventions.

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.

Failure mode and Effect Analysis of personal fall arrest system under the intuitionistic fuzzy environment

Background and aims

Intuitionistic fuzzy sets (IFS) theory is more powerful than classic fuzzy sets theory in handling uncertainty. A new approach for Failure Mode and Effect Analysis (FMEA) was developed based on IFS and group decision-making (known as IF-FMEA) for investigating Personal Fall Arrest System (PFAS).

Method

FMEA parameters, including occurrence, consequence, and detection, were re-defined based on a seven-point linguistic scale. Each linguistic term was associated with an intuitionistic triangular fuzzy set. Opinions on the parameters were gathered from a panel of experts, integrated using the similarity aggregation method, and defuzzified utilizing the center of gravity approach.

Results

Nine failure modes were identified and analyzed using both FMEA and IF-FMEA. The risk priority numbers (RPNs) and prioritization obtained from the two approaches were different, highlighting the importance of using IFS. The highest RPN was associated with the lanyard web failure, while the failure of the anchor D-ring had the least RPN. Detection score was higher for metal parts of the PFAS, suggesting that failures in these parts are harder to detect.

Conclusion

In addition to being economical in terms of calculations, the proposed method was efficient in handling uncertainty. Different parts of a PFAS create different levels of risk.

Determining performance shaping factors to assess human error in the emergency response team in chemical process industries: a case study

Objectives. Human error (HE) plays a crucial role in the occurrence of accidents in chemical process industries (CPIs). Emergency response team (ERT) members are predisposed to HEs due to the nature of their work. The HE potential is influenced by the performance shaping factors (PSFs). Managing PSFs can diminish the human error probability (HEP) and consequently increase the emergency response success chance. This article aimed to determine the PSFs for ERT members in CPIs. Methods. First, an initial list of PSFs was searched and classified within human reliability analysis methods and studies. Then, an expert panel of the emergency management system was utilized to identify, classify and weight the initial PSFs. The fuzzy Delphi method and content analysis technique were applied to summarize and categorize the PSFs. Results. The results of the study showed that 11 PSFs had greater impacts on the ERT members' error potential. Findings revealed that stress and physiological stressors, competency, and team and organization were the three most important PSFs. Conclusion. The most important and relevant PSFs can be effectively used in accurate HE assessment of ERT members in CPIs.

Human reliability analysis in deep excavation projects using a fuzzy Bayesian HEART-5M integrated method: case of a residential tower in north Tehran

Objectives. Numerous labourers lose their lives or suffer from injuries and disabilities yearly due to the lack of safety enforcement in construction projects and accidents caused by excavation collapses. The identification and ranking of human errors have always been a central concern in civil engineering. Previous studies on excavation work and related risks have focused on retaining structure methods, while human errors may be a significant contributor to accidents and near misses. Methods. This study identified human errors in deep excavation projects using hierarchical task analysis (HTA) and a systematic human error reduction and prediction approach (SHERPA). Results. The fuzzy Bayesian human error assessment and reduction technique (HEART)-5M method was implemented to determine the human error probability (HEP) for all case-study tasks. Critical tasks were obtained as 'drainage system execution', 'water and wastewater pipes', 'gas pipes', 'checking cracks in surrounding buildings' and 'checking soil slippage' with probability levels of 0.46, 0.44, 0.44, 0.37 and 0.37, respectively. Finally, remedial measures were presented for crucial tasks. Conclusions. Six unbiased experts approved the model's desirability. The suggested approach can serve as a valuable guide for all project stakeholders in identifying, evaluating and taking corrective actions in similar projects.

A comprehensive method for the quantification of medication error probability based on fuzzy SLIM

Medication errors can endanger the health and safety of patients and need to be managed appropriately. This study aimed at developing a new and comprehensive method for estimating the probability of medication errors in hospitals. An extensive literature review was conducted to identify factors affecting medication errors. Success Likelihood Index Methodology was employed for calculating the probability of medication errors. For weighting and rating of factors, the Fuzzy multiple attributive group decision making methodology and Fuzzy analytical hierarchical process were used, respectively. A case study in an emergency department was conducted using the framework. A total number of 17 factors affecting medication error were identified. Workload, patient safety climate, and fatigue were the most important ones. The case study showed that subtasks requiring nurses to read the handwritten of other nurses and physicians are more prone to human error. As there is no specific method for assessing the risk of medication errors, the framework developed in this study can be very useful in this regard. The developed technique was very easy to administer.

Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model

Liquified natural gas (LNG) as a marine fuel has gained momentum as the maritime industry moves towards a sustainable future. Since unwanted LNG release may lead to severe consequences, performing quantitative risk assessment (QRA) for LNG bunkering operations has become mandatory according to some regulations. Human error is a main contributor to the risks, and the human error probabilities (HEPs) are essential for inclusion in a QRA. However, HEPs data are unavailable in the LNG bunkering industry so far. Therefore, this study attempts to infer HEPs through on-site safety philosophical factors (SPFs). The cognitive reliability and error analysis method (CREAM) was adopted as a basic model and modified to make it suitable for HEP assessment in LNG bunkering. Nine common performance condition (CPC) indicators were identified based on the fuzzy ranking of 23 SPF indicators (SPFIs). A Bayesian network (BN) was built to simulate the occurrence probabilities of different contextual control modes (COCOMs), and a conditional probability table (CPT) for the COCOM node with 19,683 possible combinations in the BN was developed according to the CREAM’s COCOM matrix. The prior probabilities of CPCs were evaluated using the fuzzy set theory (FST) based on data acquired from an online questionnaire survey. The results showed that the prior HEP for LNG bunkering is 0.009841. This value can be updated based on the re-evaluation of on-site SPFIs for a specific LNG bunkering project to capture the dynamics of HEP. The main innovation of this work is realizing the efficient quantification of HEP for LNG bunkering operations by using the proposed fuzzy BN-CREAM model.