P. Dimas, D. Nikolopoulos, and C. Makropoulos, Simulation framework for pipe failure detection and replacement scheduling optimization, e-Proceedings of the 5th EWaS International Conference, Naples, 556–563, 2022.
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 on a real world-based case study.
Our works referenced by this work:
|1.||D. Nikolopoulos, G. Moraitis, D. Bouziotas, A. Lykou, G. Karavokiros, and C. Makropoulos, Cyber-physical stress-testing platform for water distribution networks, Journal of Environmental Engineering, 146 (7), 04020061, doi:10.1061/(ASCE)EE.1943-7870.0001722, 2020.|