Human reliability analysis—accounting for human actions and external factors through the project life cycle

Abstract

Airplanes, ships, nuclear power plants and chemical production plants (including oil & gas facilities) are examples of industries that depend upon the interaction between operators and machines. Consequently, to assess the risks of those systems, not only the reliability of the technological components has to be accounted for, but also the 'human model'. For this reason, engineers have been working together with psychologists and sociologists to understand cognitive functions and how the organisational context influences individual actions. Human Reliability Analysis (HRA) identifies and analyses the causes, consequences and contributions of human performance (including failures) in complex sociotechnical systems. Generally, HRA research is concentrated in modelling workers' performance in the "sharp-end", assessing the ones directly involved in handling the system, especially operators. However, in theory, a reliability analysis can be applied to any kind of human action, including those from designers and managers. This research will evaluate a way of conducting HRA in the design process, as previous research has demonstrated that design failure is the predominant contributor to human errors (Moura et al., 2016). Bayesian Network (BN)-a systematic way of learning from experience and incorporating new evidence (deterministic or probabilistic)-is proposed to model the complex relationships within cognitive functions, organisational and technological factors. Conditional probability tables have been obtained from a dataset of major accidents from different industry sectors (Moura et al. 2017), using a classification scheme developed by Hollnagel (1998) for an HRA method called CREAM-Cognitive Reliability and Error Analysis Method. The model allows to infer which factors most influence human performance in different scenarios. Also, we will discuss if the model can be applied to any human actions through the project life cyclesince the design phase to the operational phase, including their management. The expected results of such study can be either qualitative or quantitative, depending on the industry sector best practice, data availability and regulatory requirements. Quantitative results for HRA means giving the human performance a number, a probability of occurrence-the so-called Human Error Probability (HEP). This gives decision-makers the

Identifying and Evaluating the Essential Factors Affecting the Incidence of Site Accidents Caused by Human Errors in Industrial Parks Construction Projects

“…For example, Azhdari [ 23 ] reached the conclusion that increasing the effectiveness of staff training and improving employee performance monitoring play a key role in reducing the incidence of human error events in the industry. Morais et al [ 27 ] introduced factors such as inadequate skills, lack of sufficient information, inadequate quality control, inadequate communication, inadequate working hours, design problems, and management issues as causes of human error. Amiri et al [ 10 ] also found that factors such as burnout and heat are considered as serious factors in the occurrence of accidents in construction industry projects.…”

“…Morais et al [ 27 ] studied the analysis of human reliability of human actions and external factors through the project life cycle. They cited factors such as inadequate skills, insufficient information, inadequate quality control, inadequate communication, inadequate working hours, design problems, management issues, social pressures, and inadequate task allocation as factors in human error.…”

Distortion Models for Estimating Human Error Probabilities

“…In this section we summarise the work carried out in Morais et al (2019a,b), Moura et al (2016) to estimate human error probabilities. We begin (Section 4.1) by recalling the MATA-D dataset built in Moura et al (2016) as well as the procedures carried out in Morais et al (2018) using Bayesian networks (Section 4.2) and in Morais et al (2019a,b) using credal networks (Section 4.3). Finally, we introduce our proposal in Section 4.4: the use of distortion models in order to overcome some of the shortcomings of the papers above.…”

“…Following (Mkrtchyan et al, 2015), Morais et al constructed a Bayesian network summarising the interactions between the different factors (Morais et al, 2018). For this aim, each factor is represented by a node, and the arcs represent the significant conditional dependences between them, based on the analysis performed in Moura et al (2017a,b).…”

Using Process Mining and Control Charts to Improve the Reliability of Road Freight Transportation

“…The influence of the human factor in the services sector is still dominant, and the efficiency of a service company as a whole critically depends on well-coordinated, motivated work of employees, and partner relations with each other, as well as attitude to their company. In turn, the organizational structure of the company, the attitude of management to employees, the schedule and workplace conditions, the nature of operations leave a serious imprint on the personnel functioning [3]. Today, many studies, methods and approaches are available devoted to analyzing the company business, optimizing the personnel work, improving the efficiency of business processes, which originate from F. Taylor's early 20th-century papers on the scientific organization of labor [4,5,6].…”