AHP-SLIM

Abstract

This thesis proposes a new method for estimating human error probability (HEP) and validates this method through experimentation. In the first section, the new method is suggested and applied to human errors (HEs) in nuclear power plants. In the second section, driver’s errors probabilities (DEPs) are estimated using the method. In last, the DEPs are also estimated through other two methods, and then they are compared with the results of the second part. Accidents in industrial settings are mainly a result of HEs; relevant fields that consider HEs have recently increased in number. For this reason, numerous methods to quantitatively estimate their probabilities; i.e. HEPs have been developed. However, it is difficult to obtain empirical data, which forces a heavy reliance on the judgment of experts in the field. During the process of judgments by experts, subjectivity plays an important role, causing difficulties in assuring consistency. To overcome this problem and to obtain a more accurate estimation, this part suggests a new and simple method. This method is referred to as AHP-SLIM, a type of HEP estimation using analytic hierarchy process (AHP), which quantifies the subjective judgment and confirms the consistency of collected data. This new method also uses the process of expert judgment within success likelihood index method (SLIM). Through applying this method to the tasks of NUREG/CR-3518, which are related to nuclear power plants, we confirm the validity of the method. Next, DEPs are estimated using AHP-SLIM. Car crashes are the leading cause of death for persons of all age groups. Human-related factors are known to be the primary causal factor of such crashes, rather than vehicle- and environmental-related factors. In many areas of car engineering, psychology, human factor, etc., there are various studies that analyze the driver’s behavior and characteristics while driving in order to reduce car crashes. However, there are almost no studies which p...