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Community and infrastructure resilience against natural and man-made hazards is paramount for the well-being of modern societies. To adapt to the fast-changing world, having communities that can effectively respond to the continuously changing (physical and social) environment is essential. Despite the existing literature on resilience definition and estimation, few frameworks and associated tools can effectively help decision-making. In addition, these tools are usually not well integrated into the community and infrastructure management processes so that decision-makers and authorities can effectively use them. This paper aims at developing a resilience-based risk assessment approach at the community level. It combines the risk analysis parameters with the intrinsic resilience of the community. The proposed approach offers essential insights into the quantitative resilience analysis of communities at different scales and for different natural hazards. It enables the combination of the risk parameters (hazard, exposure, and vulnerability) with the inherent resilience of all the systems that constitute a community. This paper also presents an easy-to-interpret tool for visualizing the resilience results obtained from the introduced approach. It translates the paper's scientific contribution into an interactive and visualization instrument that can ultimately support policymaking to ensure their communities' short and long-term resilience under known and unknown hazardous events.

This chapter introduces the role of machine learning (ML) in resilience engineering and discusses actual cases of emergencies in which ML contributed positively. To identify its benefits within the resilience-relevant aspects (social, economic, infrastructural, institutional, environmental, and communitywise), the role of ML in various disaster management applications is analyzed, including model identification, emergency detection, and solution generation. The problem of data scarcity in model identification is presented. The application of ML in different fields of emergency detection (e.g., physical, virtual) is highlighted. Finally, the effectiveness of ML in solution generation to support human decision making is evaluated. Real examples are included in which machines exceed humans in providing solutions.

The multitude of uncertainties of both natural and man-made disasters have prompted an increased attention in resilience engineering and disaster management. To overcome the effects of disastrous events, such as economic and social effects, modern communities need to be resilient. Natural disasters are unpredictable and unavoidable. While it is not possible to prevent them and protect individuals and societies against such disasters, modern communities should be prepared by incorporating both pre-event (preparedness and mitigation) and post-event (response and recovery) resilience activities to minimize the negative effects after a severe event. Resilience indicators may be fundamental to help the planners and decision-makers to develop strategies and action plans for making communities more resilient. This chapter presents a quantitative approach to estimate the resilience and resilience-based risk at the state level. In the proposed method, the resilience-based risk is a function of resilience, hazard, and exposure. To evaluate the resilience parameter, data provided by the Sendai Framework for Disaster Risk Reduction (SFDRR) are used. The framework is developed using resilience indicators with the primary goal of achieving disaster risk reduction. To use those indicators in the resilience assessment, it is necessary to define the impact and the contribution of each indicator towards resilience. To do that, two possible methods to combine and weight the different SFDRR indicators are presented: Dependence Tree Analysis (DTA) and Spider Plot Weighted Area Analysis (SPA). The proposed approach allows the decision-makers and governments to evaluate the resilience and the related resilience-based risk (RBR) of their countries using available information.

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.

Telecommunication infrastructures provide essential services to urban communities. During an emergency caused by a seismic event, their functionality is crucial to guarantee communication among users and also facilitate search and rescue operations. In this paper, a methodology to model and quantify the seismic vulnerability and resilience of wireless telecommunication networks is presented. The case study is a virtual city resembling a typical medium-sized European city. Three separate networks managed by as many mobile network operators have been designed out of information gathered from a crowdsourced database and visual satellite inspections. The interdependence with the built environment is taken into account by associating the failure of network components with the collapse of the buildings hosting them. Three vulnerability indexes for the resilience analysis of urban telecommunication networks have been defined considering failed telecommunication towers, throughput, and the number of users per base station. They have been calculated after four seismic scenarios with different characteristics and intensities.

Most part of valuable art goods are conserved in the Museums, which are in charge of their maintenance and their exposure; some of them, however, have an outdoor location, enriching the artistic and touristic worth of the towns which host them. The art works have an intrinsic vulnerability, due to their irregular shape, slenderness, fragility and – as concerns some sculptures of historical centers – to their oldness. The outdoor artifacts, however, have further hazard sources, since they can hardly be guarded, and they are subjected to possible vandalism actions and lunatic or terrorist attacks. In this work the dynamic response of artifacts to blasts of assigned intensity are investigated on a case-study, i.e. the Fountain of Neptune, located in “Piazza della Signoria”, in Florence. The Fountain of Neptune is a marble and bronze opus made by Bartolomeo Ammannati between 1560 and 1565. The main character of the Fountain, Neptune, is a marble statue 5.7 meters tall, with a weight equal to 11.5 ton. A preliminary laser scanner survey has been made to achieve the geometrical representation of the statue. The considered load condition consists of an explosion caused by 10 kg of TNT placed at 8 meters from the Fountain. The dynamic behavior of the complex under the assumed load has been represented through a numerical analysis, by considering the main statue only - since it is the slenderest element of the complex – and by assuming it as simply supported on the pedestal, without any connector. The obtained results showed the vulnerability of the statue to the assumed blast, and pointed out the role plaid by the contact assumption on its dynamic response.

Seismic damage simulation at the regional scale provides valuable information that can facilitate decision making and disaster mitigation, reducing both human and economic losses. Building damage assessment after an earthquake event is one of the crucial activities in seismic loss estimation. The amount of generated debris and the associated effects on critical infrastructures are also essential information to evaluate. Indeed, as cascading consequence of debris accumulation, the road network can be interrupted. This entails an overall increase in the average number of people who have difficulty to evacuate, with a significant risk that some residents cannot evacuate at all. This study presents a methodology to assess the area covered by debris generated by the collapse of masonry buildings as function of the geometric characteristics of the structure. The proposed methodology allows the estimation of debris volume and can provide decision-makers with a tool to explore the community response to a disruptive event, quantifying the performance of critical infrastructure and planning better resilience strategies to minimize losses and recovery time.

Earthquake-induced collapse of building structures has been effectively controlled in recent years, however, failure of non-structural components and secondary disasters induced by falling debris remain a serious matter, making the outdoor evacuation more dangerous. A pedestrian evacuation simulation framework considering earthquake-induced falling debris is proposed herein, and the associated prediction model to calculate the falling debris distribution is also established. Moreover, experiments are conducted to quantify the influence of falling debris on pedestrian movement. After establishing the evacuation environment, a social force model is adopted to conduct the evacuation simulation. A case study of the teaching area located in Tsinghua University campus is performed using the proposed method for regional evacuation simulation. The results show that when considering the motion of debris after hitting the ground, the width of road covered by debris may be more than twice the first landing distance of debris, especially in densely built-up areas. Besides, whether the debris exists or not has more influence on the total evacuation time. The proposed method can assist with the identification of high-risk areas among the evacuation roads, as well as the provision of scientific basis for future urban planning and emergency evacuation drill development.

Recent events of bridge collapses, e.g. in Genoa (Italy) on August 14th 2018 and in Kolkata (India) on September 4th 2018, have focused the public interest on the infrastructures’ safety for their consequences in terms of fatalities and injuries, but also of economy and social losses. Indeed, focusing on reinforced concrete structural components such as beams, they are subjected during their service life to different loading conditions that may affect their durability and efficiency. This can reduce the structural safety over time until its complete degradation up to the ultimate limit state. In particular, reinforced concrete elements can develop cracking conditions due to tensile stresses that are normally absorbed by the reinforcement. However, such cracking conditions can develop and propagate leading to the exposure of the reinforced concrete element to the aggression of external agents such as chlorides up to collapse. In this work, collapse tests on reinforced concrete beams are reproduced in laboratory through nonlinear numerical simulations. The numerical outcomes will be also compared to available monitoring data collected by distributed fiber optics sensors and image correlation techniques to monitor the state of cracking and its propagation in the thickness.

This study examines the effect of corrosion on reinforced concrete structural elements of a bridge overpass in seismic affected areas. Starting from a literature review to identify the existing deteriorating models, a case study is selected to perform seismic analyses. It allows to assess the structural response at different levels of reinforced concrete degradation due to corrosion. The selected case study is a frequently used structural system with several existing examples in critical infrastructures all around the world. Consequently, an in-depth analysis of the degradation of this type of system, with the identification of the most appropriate time for restoration and the possible retrofitting strategies may be of interest. The bridge is assumed to be located in Sicily, the largest island in Southern Italy. In particular, a site close to the Strait of Messina has been selected with a high seismic hazard. According to the current Italian standard for constructions, it belongs to the first seismic category among four. Indeed, a strong earthquake destroyed all the city in 1908. A finite element model of the bridge overpass is prepared to perform the seismic analyses. Different levels of degradation have been simulated by using a deterministic approach. They include the reduction of the steel reinforcement areas, the bond decreasing and the steel strength losses due to corrosion. The position of the bridge concerning the sea is also considered because it can also affect the steel reinforcement degradation due to pitting corrosion. Finally, retrofitting strategies to improve the seismic performance of the bridge are evaluated to preserve it from demolishing and rebuilding.

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.

Many classes of engineering problems focus on the process of calibrating mathematical models using observed data. The enormous progress of scientific computation and data-mining techniques ...

Seismic vulnerability assessment of existing buildings is an essential process for earthquake disaster management. It can support decision-makers to estimate critical response parameters and improve them. While several seismic vulnerability assessment techniques have been introduced over the past years, a detailed formulation to measure the vulnerability index of existing buildings along with the operative procedure is still lacking. Current standards and literature worldwide mostly propose simplified approaches (e.g. based on limited engineering analyses, visual inspection) to perform a rapid vulnerability assessment that can be useful when a large number of buildings should be analyzed. This paper presents a methodology to perform the seismic vulnerability assessment of existing buildings at collapse, analyzing a standard vulnerability index, and proposing alternative formulas. The methodology combines field investigations and experimental tests with nonlinear structural analyses. It has been applied to a case study that consists of an existing reinforced concrete school building to analyze and compare the proposed indices with respect to different ground motion selection procedures. Furthermore, a detailed comparison with alternative vulnerability indices from literature has been also performed.

Bridges and critical transport infrastructure are primary assets and systems that underpin human mobility and activities. Loss of bridge functionality has consequences on entire transport networks, which are also interconnected with other networks. Cascading events then unfold in the entire system of systems, leading to significant economic losses and societal disruption to business and society. Recent natural disasters have revealed the vulnerabilities of bridges and critical assets to diverse hazards (e.g. floods, blasts, earthquakes), some of which are exacerbated due to climate change. The assessment of bridge and network vulnerabilities by quantifying their capacity and functionality loss and adaptation to new requirements and stressors is thus of paramount importance. The aim of this work was try to understand the main compound hazards, stressors and threats that have short- and long-term impacts on the structural capacity and functionality of bridges and the impact of bridge closures on network operability. The main drivers of bridge restoration and reinstatement were also considered. Loss of performance, driven by redundancy and robustness of a bridge, is the first step to be considered in the overall process of resilience quantification. Resourcefulness is the other main component of resilience analysed in this work. Peer Reviewed Objectius de Desenvolupament Sostenible::13 - Acció per al Clima Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

Urban areas reveal particularly vulnerable due to the high concentration of people and, in many cases, their hazard-prone location. Indeed, according to data from the United Nations, about 2/3 of the population will live in large cities by 2050, and the majority of the world’s cities are highly exposed to disasters. This paper presents a computational framework to assess the seismic vulnerability and the damage of residential building portfolio in urban areas. First, a surrogated model is proposed to estimate the global capacity of building structures. Monte Carlo simulations are implemented to take into account the uncertainties associated with the material, mechanical, and geometrical parameters. The proposed approach is validated through nonlinear finite element models and a real case study. Then, the proposed computational framework is implemented and applied to a virtual city that is envisioned for being representative of a typical Italian residential building stock. The main achievement of this work is to introduce a new simplified approach for large scale structural analyses to limit the computational efforts while providing reasonable results.

Over the last decade, the topic of regional resilience has drawn the attention of public authorities due to the increasing number of natural disasters. The absence of a practical and concrete methodology makes it extremely difficult to evaluate resilience at the regional scale, which involves several concepts such as economics, social sciences, environment, etc. This paper proposes an indicator-based approach to assess the resilience assessment of Italian regions. A set of twelve indicators has been selected among publicly available census data. A time window of ten years was considered in the analysis. Three different resilience indexes were calculated for each region. The first is an overall measure of resilience, while the other two represent resilience during the emergency and the restoration phase following a disaster. Results highlight fundamental aspects that have a higher impact on regional resilience and can be used by decision-makers to effectively allocate resources. The procedure has also been extended to evaluate the regional epidemic risk which can be used as a preliminary tool to develop risk mitigation strategies against biological hazards.

This paper focuses on the vulnerability assessment of a civic tower built in 1512, which is now considered a national monument. It is the original bell tower of S. Ambrogio church that was destroyed in 1809. Experimental investigations have been carried out on this historical tower. First, detailed investigations have been carried out to identify the geometry of the tower as well as the mechanical features of the constituting materials. Then, ambient vibration tests have been applied using five Micro Electro-Mechanical Systems (MEMS) sensors to detect of the main dynamic features, e.g., modal parameters and damping. Two output-only identification methods, including Frequency Domain Decomposition and Random Decrement Techniques, have been used. The outcomes of the modal identification have been employed to inform the FE model. The numerical analysis can be used for vulnerability assessment, providing a valuable picture of possible damage evolution, tower collapse mechanism, and subsequently, useful hints for the execution of structural retrofitting strategies.

This chapter highlights some of the main drivers and challenges for research on risk control of critical infrastructure systems, relates to selected similar road-mapping approaches for motivating the overarching main questions posed to the working groups, and attempts to formulate some more operational sample questions for each workshop topic. Resilience, originally discovered as the stunning property that allows individuals to better survive even the worst conditions in early youth, by has made its way through many disciplines as a stimulating concept. Resilience is understood ultimately as a systemic property referring to a system functionality behavior of interest. Acceptance refers to the overall behavior in all-resilience cycle phases but typically also includes the individual acceptance of each phase and for all types of potential disruptions. A wide range of methodologies was covered from conceptual frameworks, engineering-analytical approaches, component and large-scale structure resilience and their simulation, modeling, and simulation to network modeling sciences, machine learning, and artificial intelligence expert systems.

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.

This work presents an overview of some of the most promising technologies for the structural health monitoring (SHM) of in-service tunnels. The common goal of damage or unusual behaviour detection is best pursued by an integrated approach based on the concurrent deployment of multiple technologies. Typically, traditional SHM systems are installed in problematic or special areas of the tunnels, giving information on conditions and helping manage maintenance. However, these methodologies often have the drawbacks of forcing the interruption of traffic for SHM system installation and monitoring only selected portions. Alternative solutions that would make it possible to keep the tunnel in normal operation and/or to analyse the entire infrastructure development through successive and continuous scanning stages, would be beneficial. In this paper, the authors will briefly review some traditional monitoring technologies for tunnels. Furthermore, the work is aimed at identifying alternative solutions, limiting or avoiding traffic interruptions.

Availability of resources is one of the primary criteria for communities to attain a high resilience level during disaster events. This paper introduces a new approach to evaluate resourcefulness at the community and national scales. Resourcefulness is calculated using a proposed composite resourcefulness index, which is a combination of several resourcefulness indicators. To build the resourcefulness index, resourcefulness indicators representing the different aspects of resourcefulness are collected from renowned literary publications. Every indicator is assigned a measure to make it quantifiable. Time-history data for the measures are needed to perform the analysis. While these data could be obtained from different sources, acquiring a full set of data is quite challenging. Hence, to account for missing data, the Multiple Imputation (MI) and the Markov Chain Monte Carlo (MCMC) data imputation methods are adopted. The data are then normalized, assigned weights, and aggregated to obtain the resourcefulness index. A case study is performed to demonstrate the applicability of the approach. The resourcefulness indexes of two countries, namely the United States and Italy, are evaluated. Results show that resourceful communities/countries are more resilient during disaster events as they have more tools to come up with solutions. It is also shown that knowing the current resourcefulness level helps in better identifying what aspects should be improved.