Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil

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@Article{journals/acisc/OnyeloweJOOA21,

• author = "Kennedy C. Onyelowe and Fazal E. Jalal and Michael E. Onyia and Ifeanyichukwu C. Onuoha and George U. Alaneme",
• title = "Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil",
• journal = "Applied Computational Intelligence and Soft Computing",
• year = "2021",
• pages = "6686347:1--6686347:17",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• bibdate = "2021-09-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/acisc/acisc2021.html#OnyeloweJOOA21",
• URL = "https://downloads.hindawi.com/journals/acisc/2021/6686347.pdf",
• DOI = "doi:10.1155/2021/6686347",
• size = "17 pages",
• abstract = "Gene expression programming has been applied 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 use, and management lapses. The evolution of sustainable green materials and optimisation and soft computing techniques have been deployed to improve on the deficiencies being suffered in the abovementioned areas of design and construction engineering. 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 behaviour of the soil strength parameters using the mutative and evolutionary algorithms of GEP. The input parameters were HARHA, liquid limit (), (plastic limit , plasticity index , optimum moisture content (), clay activity (AC), and (maximum dry density (omega-max) 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 optimisation 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.",
• notes = "Department of Civil and Mechanical Engineering, Kampala International University, Kampala, Uganda",

}

Genetic Programming entries for Kennedy C Onyelowe Fazal E Jalal Michael E Onyia Ifeanyichukwu C Onuoha George U Alaneme

Citations