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dc.contributor.author Ranathunga, RJKPN
dc.contributor.author Sampath, KHSM
dc.contributor.editor Mallikarachchi, C
dc.date.accessioned 2023-01-26T09:03:30Z
dc.date.available 2023-01-26T09:03:30Z
dc.date.issued 2022
dc.identifier.citation ****** en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/20301
dc.description.abstract Rice 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.language.iso en en_US
dc.publisher Department of Civil Engineering, Faculty of Engineering, University of Moratuwa en_US
dc.subject Artificial neural networks en_US
dc.subject Geotechnical properties en_US
dc.subject Multiple regression analysis en_US
dc.subject Rice husk ash en_US
dc.title Utilisation of rice husk ash for soil stabilisation en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Civil Engineering en_US
dc.identifier.year 2022 en_US
dc.identifier.conference Civil Engineering Research Symposium 2021 en_US
dc.identifier.place Katubedda en_US
dc.identifier.pgnos pp. 15-16 en_US
dc.identifier.proceeding Proceedings of the Civil Engineering Research Symposium 2022 en_US
dc.identifier.email [email protected] en_US


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  • CERS - 2022 [34]
    Civil Engineering Research Symposium 2022

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