Rising levels of seas and oceans due to global warming could drastically affect the daily lives of residents in coastal belts and lowland areas. Many of the most heavily populated regions in the world have been developed on the shorelines. Sea-level rise could directly affect the serviceability of urban structures and infrastructures of coastal regions; effects may include intrusion of salt water into drinking water resources, submergence of roads and railways, flowing of seawater into wastewater networks, and exacerbating land subsidence. These reasons have urged climate-change and infrastructure resilience researchers to focus on methods for prediction and prevention of SLR effects on urbanization systems. Most of the studies have concentrated on environmental aspects or modeling of flooding, however, there is a lack of research on behavior of urban lifelines for long-term planning. Hence, the resilience of coastal cities has become of more interest in recent years. This paper presents a meta- analysis and review of existing literatures on the impacts of SLR on civil infrastructure. We categorize these impacts based on different types of infrastructures (e.g. water, transportation, energy) and regions. The review provides i) an intensive coverage of the existing literature on adaptations ii) an exploration of current gaps and challenges in civil infrastructures in different regions of the world and iii) the engineering perspective of SLR besides managing directions to be useful for engineers, advisory committees, policy makers, and scholars for future studies.

This study proposes a network analysis framework for characterizing infrastructure resilience in the aftermath of disasters through the use of consumers’ service disruption information. In the presented framework, the notion of “peers” is used to construct the network models of consumers experiencing service disruption in the aftermath of a disaster to understand the type and extent of infrastructure damages and specify disruption patterns. Data related to electricity disruption in Bhaktapur, Nepal, in the aftermath of the 2015 Gorkha Earthquake is used to construct the network models of consumers’ networks at different points in time in the aftermath of the disaster. The created models are then used in network analysis for examining the network topological characteristics (such as clustering) and specifying the attributes of service disruption. The contribution of this paper lie in: (i) the development and validation of a novel network from disruption information, (ii) identify the extent of infrastructure disruption, type of damage, and recoverability from changes in network topology over time.

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