Objective Resilience: Objective Processes

Civil engineering infrastructure systems exhibit uncertainties because their various properties (e.g., strength) cannot be identified with sufficient accuracy. The natural consequence of these uncertainties is the introduction of risk. This chapter explores the issue of uncertainties in objective resilience applications, addressing objective resilience problems involving uncertainties through a Monte Carlo simulation approach. A general simulation methodology is described to achieve this objective; the basic algorithm of the spectral representation method is presented as a universal tool for uncertainty quantification. Three examples are given that demonstrate the use of the spectral representation method to generate realizations of uncertain seismic ground motion acceleration time histories, uncertain wind velocity time histories, and uncertain soil properties.

This chapter presents the traditional reliability metrics and their applicability, focusing on resilience, one of the extended reliability metrics. A detailed overview of potential multiple hazards and methods of predicting and quantifying them is given. Existing definitions of resilience are reviewed, as well as methods of assessment and quantifications. In the context of robustness and restoration ability, quantifications of resilience are proposed for both nonrepairable and repairable systems under multiple hazards. A detailed discussion of system recovery is included. Specifically, importance measures are recommended to identify components' failures and repairs that have the greatest impact on system performance.

Envision translates communitywide sustainability and resilience goals into actions on infrastructure projects. This chapter discusses applications of Envision to rate resilience in terms of risk management, addressing uncertainties through planning; production and manufacturing; energy and emissions; climate change; risk and threat; service; durability and adaptability; survival: recovery and synergies; sustaining innovation; and system engineering approaches. Envision concepts have been applied during community planning and conceptual project development to develop a project scope that integrates sustainability and resilience. Also explored are sustainable practices, such as application of local materials and using local skills that have substantial benefits during recovery after a disaster, and thus, contribute to the resilience of project by enhancing resourcefulness.

This chapter provides a comprehensive review of available methods of modeling infrastructure interdependencies for quantification, prediction, and enhancement of disaster resilience. A specific characteristic of intrasystem dependency is the dependency of the functionality of an infrastructure system on the functionality of all its components, and the dependency of the functionality of a component on the functionality of all its subcomponents. Some findings of the PRAISys project are summarized to provide a comprehensive review of existing methods of modeling and assessing infrastructure interdependencies. Models and classifications are presented in a general context to highlight their essence, while specific dependencies and interdependencies between two infrastructure types are noted.

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.

This chapter investigates the importance and characteristics of system resilience. Literature on the topic of infrastructure systems interdependence and resilience is reviewed to establish meaningful definitions, classifications, and metrics that guide the research of complex systems modeling and resilience. The distinctions among resilience, risk, and vulnerability are detailed. Various resilience metrics are introduced to accurately quantify system resilience. Infrastructure systems interdependence is also discussed, and the different classifications of these systems are detailed. This chapter also introduces infrastructure systems simulation techniques and discusses tools for systems assessment and control.

This chapter presents and investigates a proposed methodology to quantitatively measure resilience in communities. It also identifies the most significant issues defined by the PPD-8 and the NFR to develop a methodological analysis for resilience. Consequently, the community lifelines are viewed as systems delivery for security, government, business, health, and supply chains. These systems constitute the base of community resilience and depend on multiple subsystems that carry out essential community functions. Also discussed is FEMA's primary mission of reducing the loss of life and property from all hazards and coordinating the national response and recovery efforts of catastrophic natural and man-made disasters.