Sustainable Soil Stabilisation Using Water Treatment Sludge: Experimental Evaluation and Metaheuristic-Based Genetic Programming

Recycling water treatment sludge (WTS) offers a sustainable solution to reduce environmental waste and enhance soil stabilisation in geotechnical applications. This study investigates the mechanical performance of soil-sludge-cement-lime mixtures through an extensive experimental program and focuses on compaction characteristics and California Bearing Ratio (CBR) values. Mixtures containing 40% soil, 50% sludge, and 10% lime achieved a CBR value of 58.7% and represented a 550% increase compared to untreated soil. Additionally, advanced predictive modelling using symbolic metaheuristic-based genetic programming (GP) techniques, including the Dingo Optimisation Algorithm (DOA), Osprey Optimisation Algorithm (OOA), and Rime-Ice Optimisation Algorithm (RIME), demonstrated exceptional accuracy in predicting CBR values. The GP-RIME model achieved an R2 of 0.991 and a mean absolute error (MAE) of 1.02 in predicting CBR values, significantly outperforming traditional regression methods. Four formulas are proposed to predict CBR values. This research highlights the dual benefits of sustainable WTS recycling and advanced modelling techniques, providing scalable solutions for environmentally friendly infrastructure development. This research aligns with global sustainability goals by valorising waste streams from water treatment plants. The reuse of sludge not only reduces landfill disposal but also lowers demand for energy-intensive binders, contributing to circular economy practice and sustainable infrastructure development.