Despite many studies on infrastructure resilience in the existing literature, there is a limited empirical understanding of disaster resilience in the context of intermittent infrastructure systems. To fill this knowledge gap, our study provides an example assessment of the resilience of Kathmandu Valley's electricity and water supply infrastructure systems in the 2015 Gorkha earthquake. The study is based on qualitative data collected over a period of one year following the earthquake, obtained through in‐depth interviews (n = 52), a focus group, and a review of secondary sources. A resilience assessment framework that includes eight factors adapted from existing studies—vulnerability, anticipation, redundancy, adaptive capacity, rapidity, resourcefulness, cross‐scale interactions, and learning culture—was used for the data analysis. The characteristics of intermittent infrastructure systems pertaining to resilience identified in this study could have important implications for engineers and decision makers in developing communities to better design and maintain infrastructure in the face of disasters.

In this paper, a system-of-systems (SoS) framework is proposed for bottom-up assessment of complex engineering projects. Two principles of SoS analysis (i.e., base-level abstraction and multilevel aggregation) are used to develop the proposed framework. At the base level, complex engineering projects are abstracted as various entities (i.e., human agents, resources, and information) whose attributes and interactions influence the dynamic behaviors of project systems. The performance of project systems at higher levels (i.e., activity level, process level, and project level) is then determined by aggregating entities at the levels below. Through the use of the proposed SoS framework, new dimensions of analysis for better understanding of the performance of engineering projects were explored. One application example of the proposed framework was demonstrated in a case study of a complex construction project. The findings highlight the capability of the proposed framework in providing a novel approach for bottom-up assessment of performance in engineering projects.