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.

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Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards can only be predicted to some extent, their impact can be managed by increasing the emergency response and reducing the vulnerability of infrastructure. In the context of risk management, the ability of infrastructure to withstand damage and re-establish their initial condition has recently gained prominence. Several resilience strategies have been investigated by numerous scholars to reduce disaster risk and evaluate the recovery time following disastrous events. A key parameter to quantify the seismic resilience of infrastructures is the Downtime (DT). Generally, DT assessment is challenging due to the parameters involved in the process. Such parameters are highly uncertain and therefore cannot be treated in a deterministic manner. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate the DT of selected infrastructure types following earthquakes. To demonstrate the applicability of the methodology, three scenarios are performed. Results show that the methodology is capable of providing good estimates of infrastructure DT despite the uncertainty of the parameters. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes in immediate post-event applications as well as to promptly recover damaged infrastructure.@en

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.

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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.

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Natural and man-made disasters have caused a significant impact on residential buildings worldwide. The damaged buildings stay unoccupiable for a period of time, called the downtime. This chapter introduces a new methodology to predict the downtime and the resilience of building structures using Fuzzy logic. Generally, the downtime can be divided into three main components: downtime due to the structural and nonstructural damage (DT1); downtime due irrational delays (DT2); and downtime due to utilities disruption (DT3). DT1 is defined by assigning a pre-defined repair time to each building component given the number of workers assigned. DT2 and DT3 are estimated using the REDiTM Guidelines, which provide good estimates of the delays incurred by irrational components and utilities disruption. The Downtime of the building is finally obtained by combining all three components. Following the downtime estimation, the resilience of the building is estimated by combining the downtime of the building (DT) and the building damage level. The latter is assessed using a rapid visual screening form designed by the authors. As a case study, the methodology has been applied to a residential building where the 1994 Northridge earthquake is selected as the hazard event.@en