Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Abstract

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate-size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.

Resilience of School Systems Following Severe Earthquakes

“…It describes the functionality of not only the school building itself but also the surrounding community's physical, economic, and social infrastructure. Similar to previous studies (Hassan & Mahmoud, 2019;Nozhati et al, 2019), the availability of each basic event is described by appropriate probabilistic mathematical functions. The basic events in the success tree analysis are connected using AND/OR gates to calculate the probability of top and intermediate events.…”

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

“…And the numbers in parenthesis mark after the equal sign represents the failure rate of each basic event. According to the hypothesis, the fault diagnosis simulation of the Bayesian network model is carried out, which is compared to the computation results based on the Binary Decision Diagrams method [34], as shown in Table 3.…”

Deep Learning-Based Damage Detection from Aerial SfM Point Clouds

“…The data also allow documentation of the sustained damage which can be used for forensic investigation to understand the event severity (e.g., estimate wind speed near the ground), distribution of damage, and engineering shortcomings. Moreover, the collected data can be analyzed and used as ground truth data to evaluate the fragility models used to predict the vulnerability of structures and infrastructure systems [1].…”