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Innovative technologies are presenting opportunities to improve resilience of energy plans for industrial and military installations. The investment rationale is complicated by uncertain future conditions across the system lifecycle, including technology, climate, economy, and others. This paper introduces resilience analytics with scenario-based preferences as follows. Risk is addressed here as the degree of disruption of priorities for investments in engineering systems. The particular concern of this paper is disruption from shifts in public values, and to evaluate the resilience of investment plans to such shifts. It recognizes resilience models as compilations of instantaneous framings of initiatives, objectives, stakeholder preferences, and uncertainties. Problem frames can be considered in series, where inputs to frames are the outputs of previous frames. Or frames can be considered in parallel, featuring joint inputs while addressing differing questions. This paper presents a case study of resilience analytics focusing on two quantitative frames. In the first frame, scenario-based preferences are used to identify combinations of factors disruptive to energy innovation at installations. In the second frame, estimation of lifecycle costs is performed with respect to factors that were identified as influential in the previous frame. [ABSTRACT FROM AUTHOR]

• System recovery under multihazards modeled using series of semi-Markov processes. • Model considers inter-event dependencies during the system recovery process. • Multihazard resilience of a residential building in Charleston, SC, is studied. • Considering inter-event dependencies leads to lesser-predicted resilience. • Reduction in resilience depends on intensities and arrival times of hazards. Civil infrastructure systems are subjected to multiple hazards, including natural and anthropogenic, that disrupt their function or the level of service offered. Estimating the function recovery of these systems (or how soon normalcy of operations will be restored) when subjected to repeated hazard events by considering the inter-event dependencies is an important problem in multihazard infrastructure resilience. However, this problem has been less addressed in the field. This paper proposes a series of semi-Markov processes model to capture the inter-event dependencies in infrastructure recovery when subjected to successive hazard events. Recovery after each new hazard event is represented by a unique semi-Markov process that models the reduced recovery rates and the increased recovery times caused by the system's incomplete recovery from the preceding event. Two novel formulations of the inter-event dependency modeling, namely Maximal Effects Dependency (considers the worst impact of two successive hazard events) and Cumulative Effects Dependency (considers the aggregated impacts of two successive hazard events), are proposed and discussed. The model is demonstrated by considering the following applications: Three-state system subjected to deterministic and random occurrences of identical hazard events; and Multihazard resilience of a building in Charleston, SC, considering earthquake and hurricane hazards. Results indicate that considering inter-event dependencies in recovery modeling can lead to lesser-predicted resilience, thereby affecting resilience-based decision-making. [ABSTRACT FROM AUTHOR]