Due to the increasing frequency of natural and man-made disasters, the scientific community has paid considerable attention to the concept of resilience engineering. On the other hand, authorities and decision-makers have been focusing their efforts on developing strategies that can help increase community resilience to different types of extreme events. Since it is often impossible to prevent every risk, the focus is on adapting and managing risks in ways that minimize impacts to communities (e.g., humans and other systems). Several resilience strategies have been proposed in the literature to reduce disaster risk and improve community resilience. Generally, resilience assessment is challenging due to uncertainty and the unavailability of data necessary for the estimation process. This paper proposes a Fuzzy Logic method for quantifying community resilience. The methodology is based on the PEOPLES framework, an indicator-based hierarchical framework that defines all aspects of a community. A fuzzy-based approach is implemented to quantify the PEOPLES indicators using descriptive knowledge instead of complex data, accounting for the uncertainties involved in the analysis. To demonstrate the applicability of the methodology, three cases with different levels of data availability are performed to obtain a resilience curve and resilience index of two out of seven dimensions of the PEOPLES framework. When numerical data does not exist, descriptive data based on expert knowledge is used as input. Results show that the proposed methodology can cope with both numerical and descriptive input data with different uncertainty levels providing good estimates of resilience. The methodology can be used as a decision-support tool to assist decision-makers and stakeholders in assessing and improving their communities' resilience for future events, focusing on specific indicators that suffer from resilience deficiencies and need improvements.

Critical infrastructures are an integral part of our society and economy. Services like gas supply or water networks are expected to be available at all times since a service failure may incur catastrophic consequences to the public health, safety, and financial capacity of the society. Several resilience strategies have been examined to reduce disaster risk and evaluate the downtime of infrastructures following destructive events. This paper introduces an indicator-based downtime estimation model for buried infrastructures (i.e., water and gas networks). The model distinguishes the important aspects that contribute to determining the downtime of buried infrastructure following a hazardous event. The proposed downtime model relies on two inference methods for its computation, Fuzzy Logic (FL) and Bayesian Network (BN), which are adapted for the current application. Finally, through a case scenario, a comparison of the two inference methods, in terms of results and limitations, is presented. Results show that both methods incorporate intuitive knowledge and/or historical data for defining fuzzy rules (in FL) and estimating conditional probabilities (in BN). The difference stands in the interpretation of the outcome. The output of the FL is a membership that defines how well the downtime fits the fuzzy levels while the BN output is a probability distribution that represents how likely the downtime is in a certain state. Nevertheless, both approaches can be utilized by decision-makers to easily estimate the time to restore the functionality of buried infrastructures and plan preventive safety measures accordingly.

Residential buildings are designed to withstand earthquake damage because it causes the buildings to be inhabitable for a period of time, called the downtime. This paper introduces a method to predict the downtime of buildings using a Fuzzy logic hierarchical scheme. Downtime is divided into three components: downtime due to the actual damage (DT1); downtime due to irrational delays (DT2); and downtime due to utilities disruption (DT3). DT1 is evaluated by relating the damageability of the building's components to pre-defined repair times. A rapid visual screening is proposed to acquire information about the analyzed building. This information is used through a hierarchical scheme to evaluate the building vulnerability, which is combined with a given earthquake intensity to obtain the building damageability. DT2 and DT3 are estimated using the REDi™ Guidelines. DT2 considers irrational components through a specific sequence, which defines the order of components repair, while DT3 depends on the site seismic hazard and on the infrastructure vulnerability. The proposed method allows to estimate downtime combining the three components above, identifying three recovery states: re-occupancy; functional recovery; and full recovery. A case study illustrating the applicability of the methodology is provided in the paper. The downtime analysis is applied to buildings with low and medium damage levels. Results from the case study show that total repair time is higher in the medium damage case, as it is expected. In both evaluations, the downtime is influenced more by irrational components and it is different in the three recovery states.