| Summary: | Identification of water network pipes susceptible to failure is a demanding task, which requires a coherent and extensive dataset that contains both their physical characteristics (i.e., pipe inner diameter, construction material, length, etc.) and a snapshot of their current state, including their age and failure history. As water networks are critical for human prosperity, the need to adequately forecast failure is immediate. A huge number of Machine Learning (ML) and AI models have been applied; furthermore, only a few of them have been coupled with algorithms that translate the failure probability into asset management decision support strategies. The latter should include pipe rehabilitation planning and/or replacement scheduling under monetary/time unit constraints. Additionally, the assessment of each decision is seldomly performed by developing performance indices stemming from simulation. Hence, in this work, the outline of a framework able to incorporate pipe failure detection techniques utilizing statistical, ML and AI models with pipe replacement scheduling optimization and assessment of state-of-the-art resilience indices via simulation scenarios is presented. The framework is demonstrated in a real-world-based case study.
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