Towards area-dependent, scenario-based multi-hazard models: Part A – Introduction to the OpenMHA methodology

The consideration of multi-hazards has become a significant challenge in probabilistic risk assessment (PRA), particularly in the aftermath of the Fukushima Daiichi accident. The causes of the Fukushima Daiichi accident and safety-related lessons learned revealed the necessity of integrating correlated external hazard events, such as seismic, flooding, and fire events, into PRA models. Evaluating these interdependencies is vital for the overall risk determination and characterization. This paper is the first in a two-part series presenting a novel approach for constructing advanced logic models within a multi-hazard PRA framework, utilizing the Open Multi-Hazards Analysis (OpenMHA) tool. OpenMHA builds area-specific fault tree logic based on detailed hazard scenarios, integrated with simulations of hazard phenomena. The proposed framework leverages a MongoDB NoSQL database to enable storage and flexible querying of spatial configurations, component specifications, failure mechanisms, and logic templates. This integration supports the automated development of both external and internal multi-hazard logic models, combining user-defined scenarios for hazard initiation and propagation. Furthermore, integrating physical simulations into the framework is essential for accurately modeling the impacts of hazards and the failure modes of structures, systems, and components (SSCs). Uncertainties in hazard parameters and environmental factors influencing SSC responses are addressed through a sequential sampling framework. The OpenMHA framework is capable of dynamically accounting for the aging of SSCs by integrating appropriate aging models, thereby effectively capturing long-term SSC degradation. The second paper in this series applies the OpenMHA framework to two case studies: seismic-induced fire and seismic-induced flooding. These applications demonstrate the framework's capability to model cascading hazards and evaluate their contributions to overall risk.