Ranathunga, R.J.K.P.N., Sampath, K.H.S.M. and Ranathunga, A.S. orcid.org/0000-0001-8746-5326 (2023) Prediction of Geotechnical Properties of Rice Husk Ash-Stabilized Soil Systems. In: 2023 Moratuwa Engineering Research Conference (MERCon). 2023 Moratuwa Engineering Research Conference (MERCon), 09-11 Nov 2023, Moratuwa, Sri Lanka. Institute of Electrical and Electronics Engineers (IEEE) , pp. 240-245. ISBN 979-8-3503-4522-3
Abstract
Rice Husk Ash (RHA) is one attractive alternative that is used as a full/partial replacement of cement/lime in problematic soil stabilization. This paper introduces statistical models; multiple regression analysis (MRA) and artificial neural network (ANN) for the prediction of Unconfined Compressive Strength (UCS), Soaked California Bearing Ratio (S-CBR), Maximum Dry Density (MDD), Optimum Moisture Content (OMC), and Plasticity Index (PI) of RHA-stabilized clayey soil. S-CBR and MDD of RHA-stabilized soil can be predicted with linear and non-linear MRA and UCS, OMC, and PI can be predicted with ANN models with prediction accuracy >95%. In the validation process, all the proposed models express prediction errors around ±25%. A Parametric Analysis (PA) and a Sensitivity Analysis (SA) were performed to evaluate the variation of UCS with the influencing input parameters. In general, the analysis suggests 6-12%RHA with a very little amount of cement (4-8%) or lime (4-9%) as the optimum mix proportion for soft soil stabilization.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Artificial Neural Network; geotechnical properties; Multiple Regression Analysis; Rice Husk Ash |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Mar 2025 17:53 |
Last Modified: | 13 Mar 2025 17:53 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/mercon60487.2023.10355529 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224332 |