Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil. (14th April 2021)

• Record Type: Journal Article
• Authors: Onyelowe, Kennedy C.; Jalal, Fazal E.; Onyia, Michael E.; Onuoha, Ifeanyichukwu C.; Alaneme, George U.
• Other Names: Daneshvar Rouyendegh (B. Erdebilli) Babak Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2021(2021)
• Publication Date: 2021-04-14
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2021/6686347
• Languages: English
• ISSNs: 1687-9724

Abstract

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 R value ) 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 ( A C ), and (maximum dry density ( δ 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 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.