Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner

This paper describes a development of regression model on predicting the hydraulic conductivity value for sedimentary residual soil mixed bentonite. The data for required parameter for model development was based on the laboratories testing such as compaction testing and hydraulic conductivity testi...

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Published in:IOP Conference Series: Earth and Environmental Science
Main Author: Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
Format: Conference paper
Language:English
Published: Institute of Physics 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201820657&doi=10.1088%2f1755-1315%2f1347%2f1%2f012048&partnerID=40&md5=80f40016007f37f13a89d06998be2aa9
id 2-s2.0-85201820657
spelling 2-s2.0-85201820657
Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
2024
IOP Conference Series: Earth and Environmental Science
1347
1
10.1088/1755-1315/1347/1/012048
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201820657&doi=10.1088%2f1755-1315%2f1347%2f1%2f012048&partnerID=40&md5=80f40016007f37f13a89d06998be2aa9
This paper describes a development of regression model on predicting the hydraulic conductivity value for sedimentary residual soil mixed bentonite. The data for required parameter for model development was based on the laboratories testing such as compaction testing and hydraulic conductivity testing. The multiple linear regression model (MLR) was selected to develop a hydraulic conductivity model (k-model). The empirical model was developed based on the 45 datasets from experimental studies encompassing range of maximum dry density (MDD), optimum moisture content (OMC), effective stress and bentonite content. The model developed in this study has met the conditions and has been verified according to the statistical validity requirements. The result shows that the fitted regression model has the reasonably good regression model for R2 = 79%. Meanwhile, the validation shows the small deviation discrepancy from Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) with a determination coefficient, R2 = 82%. In conclusion, the develop k-model in this study present a good prediction for hydraulic conductivity value. © 2024 Published under licence by IOP Publishing Ltd.
Institute of Physics
17551307
English
Conference paper

author Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
spellingShingle Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
author_facet Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
author_sort Khalid N.; Mukri M.; Zain N.H.M.; Zainuddin A.N.; Kamarudin F.
title Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
title_short Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
title_full Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
title_fullStr Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
title_full_unstemmed Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
title_sort Prediction model of hydraulic conductivity for sedimentary residual soil mixed bentonite as compacted clay liner
publishDate 2024
container_title IOP Conference Series: Earth and Environmental Science
container_volume 1347
container_issue 1
doi_str_mv 10.1088/1755-1315/1347/1/012048
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201820657&doi=10.1088%2f1755-1315%2f1347%2f1%2f012048&partnerID=40&md5=80f40016007f37f13a89d06998be2aa9
description This paper describes a development of regression model on predicting the hydraulic conductivity value for sedimentary residual soil mixed bentonite. The data for required parameter for model development was based on the laboratories testing such as compaction testing and hydraulic conductivity testing. The multiple linear regression model (MLR) was selected to develop a hydraulic conductivity model (k-model). The empirical model was developed based on the 45 datasets from experimental studies encompassing range of maximum dry density (MDD), optimum moisture content (OMC), effective stress and bentonite content. The model developed in this study has met the conditions and has been verified according to the statistical validity requirements. The result shows that the fitted regression model has the reasonably good regression model for R2 = 79%. Meanwhile, the validation shows the small deviation discrepancy from Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) with a determination coefficient, R2 = 82%. In conclusion, the develop k-model in this study present a good prediction for hydraulic conductivity value. © 2024 Published under licence by IOP Publishing Ltd.
publisher Institute of Physics
issn 17551307
language English
format Conference paper
accesstype
record_format scopus
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