Optimal Preventive Maintenance, Repair, and Replacement Program for Catch Basins to Reduce Urban Flooding: Integrating Agent-Based Modeling and Monte Carlo Simulation

• Published in: Sustainability 2023-05, Vol.15 (11), p.8527
• Main Authors: Assaf, Ghiwa, Assaad, Rayan H
• Format: Article
• Language: English
• Subjects: Agent-based models; Basins; Best practice; Channels; Climate change; Crews; Direct flow; Environmental protection; Environmental risk; Flooding; Floods; Life span; Maintenance management; Monte Carlo method; Preventive maintenance; Rain; Retention basins; Risk reduction; River channels; Runoff; Sensitivity analysis; Settling basins; Sewer systems; Sewers; Simulation; Storm runoff; Storm sewers; Stormwater; Stormwater management; Sustainability; Urban sprawl; Urbanization; Water pollution
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15118527

Urban sprawl has resulted in great losses of vegetation areas, an increase in impervious surfaces, and consequently the direct flow of stormwater into stream channels (i.e., the immediate flow of stormwater into stream channels, in comparison to the indirect flow that is represented by practices aiming to retain stormwater for a certain period of time and treat the polluted stormwater prior to flowing into the stream channels such as detention/retention basins, among others). Stormwater management systems such as catch basins (CBs) are needed to reduce the effect of stormwater runoff. Preventative maintenance, repair, and replacement of CBs are critical to achieve stormwater management best practices. Those practices prevent the blockage of the stormwater system, limit the pollutants in storm sewers, and reduce the risk of flooding. However, no preceding research studies have been conducted to model and simulate the serviceability of CBs and to determine optimal strategies for operating CBs. To that extent, this study establishes a framework to develop and validate an optimal and adaptive maintenance, repair, and overhaul (MRO) strategy for CBs. In relation to that, an agent-based model (ABM) integrated with Monte Carlo simulation was developed for all 560 CBs in New York City’s District 5 and was statistically validated using 99% confidence intervals. The MRO parameters were optimized to minimize the total cost of the system and attain the desired level of serviceability of CBs. Sensitivity analysis was conducted to guide the maintenance planning process of CBs and reveal the effect of the input parameters on the model’s behavior. In addition, ten thousand Monte Carlo iterations were simulated to derive the distributions of the defined parameters. The results proved that in order to minimize the overall cost of repair, maintenance, and replacement of CBs and attain a minimum serviceability threshold of 80%, the following optimal MRO policy needs to be implemented: having seven service crews (where service crews are human resources (i.e., MRO teams) needed to perform the required maintenance, repair, and replacement work), implementing a replacing policy, and replacing CBs after five maintenance periods. The findings revealed that the service crews represent the most critical parameter in affecting the total cost and serviceability of CBs. This research contributes to the existing literature by offering a better knowledge of the management process of CBs a