Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg

Compaction is an important engineering process that ensures the stability of soils by compressing them to a predefined strength. However, in most construction projects, particularly large-scale projects, achieving the appropriate compaction properties, such as optimum moisture content (OMC) and maxi...

Full description

Saved in:
Bibliographic Details
Main Authors: Nur Hijrah Nasuha Suzaili, Anuar Kasa
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/21453/1/JKSI_22.pdf
http://journalarticle.ukm.my/21453/
https://www.ukm.my/jkukm/si-5-2-2022/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Kebangsaan Malaysia
Language: English
id my-ukm.journal.21453
record_format eprints
spelling my-ukm.journal.214532023-04-05T03:08:43Z http://journalarticle.ukm.my/21453/ Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg Nur Hijrah Nasuha Suzaili, Anuar Kasa, Compaction is an important engineering process that ensures the stability of soils by compressing them to a predefined strength. However, in most construction projects, particularly large-scale projects, achieving the appropriate compaction properties, such as optimum moisture content (OMC) and maximum dry density (MDD), it requires time and high cost. Predicting the compaction characteristics from the Atterberg limit, which involves simpler and faster testing techniques, becomes an important task in this scenario. The purpose of this study is to study the comparison of the multiple linear regression (MLR) method with the response surface method (RSM) and artificial neural network (ANN) to determine an accurate, efficient and simple technique to predict soil compaction parameters. For this research, 29 samples were subjected to a variety of laboratory testing. All of the parameters’ statistical relationships were analyzed. In this research, techniques are used, and the findings of these studies are discussed and analysed. To see the performance and accuracy of the model, the criteria for validation of the model used are based on the value of coefficient of determination (R2), absolute mean error (MAE), mean square error (MSE) and mean square root error (RMSE). A comparison with the test data revealed that the coefficient of determination (R2) of ANN model predictions was greater than those of other models. In addition, the findings indicate that the accuracy of ANN models are superior to the statistical models MLR and RSM. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/21453/1/JKSI_22.pdf Nur Hijrah Nasuha Suzaili, and Anuar Kasa, (2022) Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg. Jurnal Kejuruteraan, 34 (SI5(2)). pp. 207-216. ISSN 0128-0198 https://www.ukm.my/jkukm/si-5-2-2022/
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Compaction is an important engineering process that ensures the stability of soils by compressing them to a predefined strength. However, in most construction projects, particularly large-scale projects, achieving the appropriate compaction properties, such as optimum moisture content (OMC) and maximum dry density (MDD), it requires time and high cost. Predicting the compaction characteristics from the Atterberg limit, which involves simpler and faster testing techniques, becomes an important task in this scenario. The purpose of this study is to study the comparison of the multiple linear regression (MLR) method with the response surface method (RSM) and artificial neural network (ANN) to determine an accurate, efficient and simple technique to predict soil compaction parameters. For this research, 29 samples were subjected to a variety of laboratory testing. All of the parameters’ statistical relationships were analyzed. In this research, techniques are used, and the findings of these studies are discussed and analysed. To see the performance and accuracy of the model, the criteria for validation of the model used are based on the value of coefficient of determination (R2), absolute mean error (MAE), mean square error (MSE) and mean square root error (RMSE). A comparison with the test data revealed that the coefficient of determination (R2) of ANN model predictions was greater than those of other models. In addition, the findings indicate that the accuracy of ANN models are superior to the statistical models MLR and RSM.
format Article
author Nur Hijrah Nasuha Suzaili,
Anuar Kasa,
spellingShingle Nur Hijrah Nasuha Suzaili,
Anuar Kasa,
Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
author_facet Nur Hijrah Nasuha Suzaili,
Anuar Kasa,
author_sort Nur Hijrah Nasuha Suzaili,
title Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
title_short Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
title_full Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
title_fullStr Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
title_full_unstemmed Pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
title_sort pembangunan model matematik lanjutan untuk meramal parameter pemadatan tanah berbutir halus dari segi had atterberg
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2022
url http://journalarticle.ukm.my/21453/1/JKSI_22.pdf
http://journalarticle.ukm.my/21453/
https://www.ukm.my/jkukm/si-5-2-2022/
_version_ 1762393394375557120