Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Structural model of factors contributing to the motivational problem of taking shortcuts at construction workplaces in the Kingdom of Saudi Arabia

An unsafe act is a type of work that involves some risk. Since more than a decade, unsafe act has accounted as a foremost cause of work-related accident, especially in the field of construction. Several attempts have been made to reduce the causes of unsafe acts; however, shortcut, which is a notable type of unsafe act, has received little attention in previous works, even though some of causal effects have been attributed to this habit. This paper aims to reveal an unexplained proportion that can be explained by different underlying causes of motivational problems to shortcuts rather than by habit. Accordingly, two structural models have been proposed based on quantitative and qualitative data from previous reviews associated with the effects on negative feeling and thinking on risk management. These models were tested using on Exploratory Factor analysis (EFA) and Confirmatory Factor Analysis (CFA) on data collected from a cross-section survey conducted at construction sites in largest cities within the Kingdom of Saudi Arabia (KSA), where large number of accidents in these cities occurred. Data of 204 respondents of the mailed questionnaires were analyzed after screening all responses. Statistical Package for Social Sciences (SPSS) (version 22) was used to test for Structural Equation Model (SEM) assumptions. The developed hypotheses were tested and model with the best fit was identified. Findings revealed that 45%–55% of the motivational problems to shortcuts were explained by the selected structural model.

Functional Information of System Components Influenced by Counteractions on Computer-Based Procedure

In an emergency condition of nuclear power plant, operators have to mitigate the accident in order to remove the decay heat and to prevent the release of radioactive material to the environment following the emergency operating procedures (EOPs). The action of operators on a component, for example, changing the parameter level of a component, which is described in a procedure step, will impact other components of the plant and the plant behavior. Nowadays, the advanced main control rooms have been equipped with the computer-based procedures (CBPs) which provide some features and benefits which are not available in paper-based procedures. However, most of CBPs do not provide information of the impact of the counteractions on each procedure step (components influenced and future plant behavior) although it is useful for operators to understand the purpose of the procedure steps before making decisions and taking the actions. This paper discusses the functional information and the method to generate the information using multilevel flow modeling (MFM) model of operator actions on some procedure steps of a simplified EOP of pressurized water reactor (PWR) plant, as an example.