Predicting nanocomposite binder improved unsaturated soil UCS using genetic programming

Abstract

The ability of the compacted soils and treated/compacted soils to withstand loads as foundation materials depends on the stability and durability of the soils. The design of such phenomena in treated soils whether as subgrade of pavements or embankments, backfills, etc., is a crucial phase of foundation constructions. Often, it is observed that soil mechanical and structural properties fall below the minimum design and construction requirements and this necessitates the stabilization in order to improve the needed properties. It can be observed that for this reason, there is a steady use of the laboratory and equipment prior to any design and construction as the case may be. In this work, genetic programming (GP) has been employed to predict the unconfined compressive strength of unsaturated lateritic soil treated with a hybridized binder material called hybrid cement (HC), which was formulated by blending nanotextured quarry fines (NQF) and hydrated lime activated nanotextured rice husk ash. Tests were conducted to generate multiple values for output and inputs parameters, and the values were deployed into soft computing technique to forecast UCS adopting three (3) different performance complexities (2, 3 and 4 levels of complexity). The results of the prediction models show that the four (4) levels of complexity GP model outclassed the others in performance and accuracy with a total error (SSE) of 2.4% and coefficient of determination (R2) of 0.991. Generally, GP has shown its robustness and flexibility in predicting engineering problems for use in design and performance evaluation.