# A Modeling of Human Reliability Analysis on Dam Failure Caused by Extreme Weather
## 2 HRA Analysis Framework Construction  
To explore the human influence factors in dam failure accidents scientifically and effectively, it is necessary to determine the research methodology and process first. [PERSON] [22; 25] proposed a four-part procedure for calculating the probability of human-caused error using THERP (Technique for human error-rate prediction) which includes familiarity, qualitative analysis, quantitative analysis, and incorporation. [PERSON] [26] builds on this by comparing the THERP method with the BBN (modeling the human factor) technique and combines it with Bayesian networks to propose the steps when applying BNN for HRA analysis. Based on these studies, it can be borrowed and used in this study to propose a more applicable HRA analysis path for dams, as shown in Figure 1.  
As shown in Figure 1, the proposed HRA analysis framework also consists of four parts, mainly improved for integration with Bayesian networks and for the reservoir dam industry. The first stage is mainly to gather relevant information. Firstly, historical dam failure information is collected to construct a human-caused failure database. The acquisition of anthropogenic data is the basis of human failure research, which generally comes from accident analysis reports, simulator test data, expert judgment data, etc. [27]. In other areas, national and foreign researchers have designed and established databases with their own distinctive features. In the nuclear power field, there is the CORE-DATA human factors database based on actual operational data in the UK, the NUCLARR human factors database in the US, which mainly collects data on the probability of hardware failure and expert judgment, and human factors databases in China, mainly in the form of data reports [28]. [PERSON]. [29] collected and established a human factors database for Chinese power systems. Ref. [30] collects and studies submarine evacuation data for maritime data and proposes a method for calculating relative human error probabilities. Human factors databases in various fields are often confidential, and since human errors in dam engineering do not only come from front-line managers and operators but can also from regulatory and policymakers, with significantly different impacts than in systems such as the nuclear industry, data from other fields are only available for reference and not for direct use. That is why it is particularly significant to construct a dam failure database in the field of dams based on historical collapse events.  
Figure 1: HRA analysis path.  
The second stage, the qualitative analysis of human-caused errors, focused on task analysis, PSF studies, and Bayesian network construction, extracting influencing factors from the historical dam failure man-made database, creating a human-failure-influencing factor set, analyzing human errors along the failure pathway, and deriving PSFs from them. The specific PSFs are used as BN nodes to explore the causal relationships among them and construct a directed acyclic graph (DAG) of the Bayesian network.  
The third stage is a quantitative analysis of the human error. Starting from Bayesian network causality, expert judgment and historical dam breach data were used to consider the interdependencies between the nodes, to populate the Conditional Probability Tables (CPT). The probability of failure is calculated based on Bayesian inference, and human intervention actions can be considered.  
The final stage is incorporation, where the obtained model is analyzed and the results are applied. In this step, the model needs to be validated through sensitivity analysis and expert judgement. After confirming the reliability of the model, the results were validated and the main human factors with high sensitivity were obtained to provide advice on dam management.