The efficient transportation of goods is vital for the economic growth of communities, making developing and maintaining seaport infrastructure an essential component of the marine transportation system. Given their geographic locations, ports are consistently at risk from natural hazards, making the resilience of port infrastructure an essential goal. Despite considerable progress in resilience research, there remains a gap in methods tailored explicitly to assessing port resilience, particularly under extreme wind events. Current approaches often do not capture the full complexity of port systems, as they tend to focus on isolated aspects, such as structural resilience. This paper introduces the PORT Resilience Framework, addressing these gaps by evaluating resilience through a comprehensive list of indicators gathered from various legitimate sources. The indicators are then organized under four comprehensive resilience dimensions: Physical Infrastructure, ICT (i.e., Information and Communication Technology) and Equipment; Organization and Business Management; Resources and Economic Development; and Territory, Environment, and Stakeholders. This classification is summarized under the acronym "PORT." This paper also introduces a method for aggregating resilience indicators by considering their performance before and after a specific hazard, transforming the data into a quantifiable Loss of Resilience index. The approach is applied to a case study, assessing the resilience of a real Terminal against wind action using real data sourced from the port management. The case study analysis revealed that human resources and quay operations were the most critical factors affecting recovery, with insufficient staffing leading to prolonged recovery periods. The study further demonstrated that post-disruption activity surges, captured by different serviceability function methodologies, often created operational bottlenecks, challenging the port's overall recovery.

The ability of a community to respond effectively to emergencies is closely linked to the wellbeing of its infrastructure. Many global infrastructures are outdated, making them particularly vulnerable to natural disasters like earthquakes. In this context, this paper introduces a simulation-based approach to measure and improve the resilience of large-scale Water Distribution Networks (WDN). We evaluate network resilience using two key metrics: the first counts the number of users who lose access to water, and the second quantifies the reduction in total water supply. Both metrics are considered under the assumption that a localized system failure happens when both water pressure and flow rate fall below certain levels. We test the network's performance under various earthquake scenarios, calculating the potential damage through fragility functions that take into account both the network's characteristics and the seismic forces involved. Our model is applied to a simulated community of 900,000 people, revealing significant correlations between the timing of the earthquake, daily water demand, and the material properties of the pipes. Additionally, we present a plan to incorporate a recovery optimization module in future work. This module aims to dynamically prioritize repair tasks based on various constraints like available manpower, equipment, and budget, with the ultimate objective of maximizing the number of residents served with adequate water pressure during the recovery process.

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.

Resilience indicators are a convenient tool to assess the resilience of engineering systems. They are often used in preliminary designs or in the assessment of complex systems. This paper introduces a novel approach to assess the time-dependent resilience of engineering systems using resilience indicators. A Bayesian network (BN) approach is employed to handle the relationships among the indicators. BN is known for its capability of handling causal dependencies between different variables in probabilistic terms. However, the use of BN is limited to static systems that are in a state of equilibrium. Being at equilibrium is often not the case because most engineering systems are dynamic in nature as their performance fluctuates with time, especially after disturbing events (e.g. natural disasters). Therefore, the temporal dimension is tackled in this work using the Dynamic Bayesian Network (DBN). DBN extends the classical BN by adding the time dimension. It permits the interaction among variables at different time steps. It can be used to track the evolution of a system's performance given an evidence recorded at a previous time step. This allows predicting the resilience state of a system given its initial condition. A mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems. Two illustrative examples are presented in the paper to demonstrate the applicability of the introduced framework. One example evaluates the resilience of Brazil. The other one evaluates the resilience of a transportation system.

Sensor-enabled infrastructure systems are destined to empower resilient communities with more intelligence and sustainability, and therefore enhancing the operational safety of physical infrastructures. Through online and onboard monitoring, the infrastructure components incorporating appropriate analytic and predictive modeling tool not only provides real-Time insight into the operational status of every system and its components, but also enables the trend prediction and timely prognosis of failure before it happens as well as early-stage diagnosis of damage in its incipiency. This paper gives an overview of the importance of structural health monitoring (SHM) for critical infrastructures. It highlights the recent shift from infrastructure monitoring to infrastructure resilience using SHM. The paper also introduces the Infrastructure Resilience Framework (IRF) that encourages the incorporation of innovative techniques, such as optical fiber sensors and machine learning techniques, in infrastructure monitoring. It defines the steps that need to be taken throughout the life cycle of infrastructure.

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.

The loss of functionality of road networks during the past Canterbury (2010-2011) and Kaikōura (2016) earthquakes has questioned New Zealand's established seismic resilience. In both events, overall bridge performance was satisfactory from a life-safety perspective. However, based on the observed undesirable sub-system performance of the damaged bridges and on the direct and indirect costs due to downtime and non-structural damage, an investigation into possible improvements of the current design philosophy was warranted. Resilience can be considered as a performance indicator that quantifies the residual functionality along with the effort in responding to the seismic event. Resilience is not being considered in the seismic codes, as traditionally their main objective has been to prevent collapse and ensure life-safety. Performance-based design, as a supplement to code objectives, does not include explicit verification of the expected functionality of the structure after the earthquake. On the other hand, resilience-based design appears as a holistic design process, which identifies and mitigates earthquake-induced risks to enable rapid recovery in the aftermath of major earthquakes. This paper presents an overview of the recovery process of the Inland Route in the aftermath of the Kaikōura earthquake. The most severely damaged bridges in the route are introduced as case studies, and the main performance and functionality issues are highlighted. Based on this, a framework to incorporate resilience concepts and measures, as key design criteria and indicators, into the structural design process is also introduced and conceptually exemplified. Applying the proposed framework during the design phase will allow the estimation of final recovery times and preliminary recovery costs of the bridge after an earthquake.

Community resilience is becoming a growing concern for authorities and decision makers. This paper introduces two indicator-based methods to evaluate the resilience of communities based on the PEOPLES framework. PEOPLES is a multi-layered framework that defines community resilience using seven dimensions. Each of the dimensions is described through a set of resilience indicators collected from literature and they are linked to a measure allowing the analytical computation of the indicator’s performance. The first method proposed in this paper requires data on previous disasters as an input and returns as output a performance function for each indicator and a performance function for the whole community. The second method exploits a knowledge-based fuzzy modeling for its implementation. This method allows a quantitative evaluation of the PEOPLES indicators using descriptive knowledge rather than deterministic data including the uncertainty involved in the analysis. The output of the fuzzy-based method is a resilience index for each indicator as well as a resilience index for the community. The paper also introduces an open source online tool in which the first method is implemented. A case study illustrating the application of the first method and the usage of the tool is also provided in the paper.