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Full Article Title: Optimizing Gravity-Fed Sewer Systems using GRG and PGSL: A Path to Cost-Effective Design The Challenge: Designing a sewer system is one of the most expensive infrastructure projects for any city. Engineers must determine the ideal pipe diameters and excavation depths (slopes) to ensure waste flows correctly under gravity. Traditionally, this is done through trial and error, which often leads to "over-designing"—using pipes that are too large or digging deeper than necessary, wasting millions in construction costs. The Study: This research applies two advanced mathematical optimization techniques to find the absolute lowest cost for a sewer network while still meeting all safety and engineering standards: GRG (Generalized Reduced Gradient): A fast, calculus-based method used to find local optimal solutions. PGSL (Probabilistic Global Search Laos): A sophisticated "global search" algorithm that uses probability to ensure the best possible solution is found across the entire network, preventing the calculation from getting "stuck" in a good-but-not-perfect answer. Key Findings: Significant Cost Savings: The study demonstrated that using PGSL and GRG can reduce total construction costs by a substantial margin compared to traditional manual design methods. Strict Adherence to Standards: The AI-driven models successfully kept the flow velocity within the required limits (to prevent sediment buildup) and ensured pipe slopes met municipal regulations. Superior Performance of PGSL: For complex, large-scale networks, the PGSL algorithm proved to be more robust, consistently finding more cost-effective designs than the GRG method alone. Conclusion: This research provides a powerful framework for civil engineers to automate and optimize urban infrastructure. By shifting from manual estimations to AI-driven optimization, municipalities and developers can build essential sanitation systems more affordably, making sustainable infrastructure more accessible for growing cities. 👉 Click here for seeing full article: https://doi.org/10.48084/etasr.10228