Abstract
The escalating risks due to climate change have heightened the need to reinforce the resilience of critical infrastructure systems, particularly those essential for human well-being, such as wastewater management systems. Rising sea levels pose a significant threat to the availability of safe sanitation. To respond to these challenges, it is crucial to enhance the resilience of these systems. In this context, this dissertation investigates the adaptation of on-site wastewater treatment and disposal systems to sea-level rise risks.
A novel quantitative resilience measure is developed based on the concepts of probabilistic risk assessment, deductive fault-tree analysis in particular, the measure is derived from logical conditions imperative for system survival and limiting impact propagation during and after disruptions. A comparative analysis with other measures is performed to investigate the validity and effectiveness of the proposed measure using a case study in South Florida.
The resilience index is then integrated into a Mixed Integer Linear Programming adaptation decision-making model. The model decides on the economically optimal adaptation portfolio while ensuring a pre-defined minimum resilience threshold across all systems. The model considers hybrid centralized and decentralized wastewater management systems configurations.The effectiveness of the proposed model is demonstrated via a case study based on a real-world setting in the Village of Key-Biscayne, Florida. A heuristic, Clustering-Based Adaptive Decomposition (CBA-Decomp) Algorithm, is developed to solve large-size problems. A simulation-based sensitivity analysis is performed to investigate the computational performance of the proposed algorithm and derive insights on implementation pathways.