Utilisation of rice husk ash for soil stabilisation

dc.contributor.authorRanathunga, RJKPN
dc.contributor.authorSampath, KHSM
dc.contributor.editorMallikarachchi, C
dc.date.accessioned2023-01-26T09:03:30Z
dc.date.available2023-01-26T09:03:30Z
dc.date.issued2022
dc.description.abstractRice husk ash (RHA) is one attractive additive that can be used for the enhancement of engineering properties of problematic soils as a full/partial replacement of cement/lime. The present study reviews the production of RHA and its basic characteristics as it relates to the performance of RHA-stabilized soils. For each soft ground improvement application, a substantial amount of time, money and effort is required for conducting extensive laboratory tests to evaluate the improvement of geotechnical properties of stabilized soil and identify the optimum mixture of additives. As an indirect approach, the present study explores the development of predictive models for geotechnical properties using multiple regression analysis (MRA) and artificial neural network (ANN) analysis. Laboratory experimental data sets from an extensive literature review were used to develop the models. The models for the prediction of Unconfined Compressive Strength (UCS), Soaked California Bearing Ratio (SCBR), Maximum Dry Density (MDD), Optimum Moisture Content (OMC), and Plasticity Index (PI) of RHA-stabilized clayey soil were proposed. It is noticed that at the prediction of S-CBR, and MDD, MRA gives better correlations with more than 95% prediction accuracy. Since MRA does not provide satisfactory performance for the prediction of UCS, OMC, and PI, ANN models were developed with R2 of more than 0.95. The proposed models were validated using the independent sets of data which is 30% of total data points. All the models express a good prediction capability with a prediction error of less than ±7.5%. A parametric analysis is performed to evaluate the variation of UCS of RHA-stabilized soil with the effect of influencing input parameters. The result of PA suggests that 6-12% of RHA in combination with a very little amount of cement (4-8%) or lime (3-6%) is the optimum mix proportion for RHA-stabilized soil ensuring the robustness and reliability of the proposed model. Hence, the proposed correlations may give easy access for facilitating the engineering decisions during the pre-feasibility assessment. Further research into the production process of RHA, the application potential of other waste materials to incorporate with RHA-cementlime binder mixtures and evaluation of indirect approaches to assess the characteristics of stabilized soil systems could lead to the utilization of RHA as a beneficial and productive alternative in soil stabilization.en_US
dc.identifier.citation******en_US
dc.identifier.conferenceCivil Engineering Research Symposium 2021en_US
dc.identifier.departmentDepartment of Civil Engineeringen_US
dc.identifier.emailpoorni.nimasha.ranathunga@gmail.comen_US
dc.identifier.facultyEngineeringen_US
dc.identifier.pgnospp. 15-16en_US
dc.identifier.placeKatubeddaen_US
dc.identifier.proceedingProceedings of the Civil Engineering Research Symposium 2022en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/20301
dc.identifier.year2022en_US
dc.language.isoenen_US
dc.publisherDepartment of Civil Engineering, Faculty of Engineering, University of Moratuwaen_US
dc.subjectArtificial neural networksen_US
dc.subjectGeotechnical propertiesen_US
dc.subjectMultiple regression analysisen_US
dc.subjectRice husk ashen_US
dc.titleUtilisation of rice husk ash for soil stabilisationen_US
dc.typeConference-Abstracten_US

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