Abstract
Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash. Pavement foundations suffer failures due to poor design and construction, poor materials handling and utilization, and management lapses. The evolution of sustainable green materials and optimization and soft computing techniques have been deployed to improve on the deficiencies being suffered in the abovementioned areas of design and construction engineering. In this work, expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in an incremental proportion to produce 121 datasets, which were used to predict the behavior of the soil’s strength parameters utilizing the mutative and evolutionary algorithms of GEP. The input parameters were HARHA, liquid limit ( w L ), (plastic limit w P , plasticity index I P , optimum moisture content ( w OMC ), clay activity (AC), and (maximum dry density (δmax) while CBR, UCS, and R value were the output parameters. A multiple linear regression (MLR) was also conducted on the datasets in addition to GEP to serve as a check mechanism. At the end of the computing and iterations, MLR and GEP optimization methods proposed three equations corresponding to the output parameters of the work. The responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. The predicted models’ performance has shown that GEP soft computing has predicted models that can be used in the design of CBR, UCS, and R value for soils being used as foundation materials and being treated with admixtures as a binding component.
Highlights
Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash
At the end of the computing and iterations, multilinear regression (MLR) and gene expression programming (GEP) optimization methods proposed three equations corresponding to the output parameters of the work. e responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. e predicted models’ performance has shown that GEP soft computing has predicted models that can be used in the design of CBR, UCS, and Resistance Value (R value) for soils being used as foundation materials and being treated with admixtures as a binding component
From the gene expression programming of California bearing ratio, unconfined compressive strength and resistance value of hydrated-lime modified expansive soil with input parameters; hydrated-lime activated rice husk ash (HARHA), liquid limit, (plastic limit, plasticity index (IP), optimum moisture content, clay activity (AC), (maximum dry density, CBR, UCS, and R value generated from series of laboratory exercise which produced 121 datasets, the following can be concluded: (1) e A-7-6 expansive soil and hydrated-lime activated rice husk were blended in varying proportions of the additive to the soil, and the modified blend specimens were tested to get the liquid limit, plastic limit, plasticity index, optimum moisture content, clay activity, maximum density, California bearing ration, unconfined compressive strength, and resistance value responses
Summary
Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash. Introduction e design, construction, and monitoring of earthwork infrastructure have been of utmost importance due to the everyday failure civil engineering facilities experience [1,2,3,4] For this reason, composite materials with special properties have been evolved to replace ordinary cement [5,6,7,8]. The evolution of soft computing in engineering has added to the efficiency of designing, constructing, and monitoring of the performance of earthworks [15,16,17,18,19] One such soft computing or machine learning method is gene expression programming (GEP).
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