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Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction

Artificial intelligence (AI) algorithms of adaptive neuro-fuzzy inference system or the adaptive network-based fuzzy inference system (ANFIS) has been deployed to predict the swelling potential (SP) of treated weak soil. The soil was treated with quicklime activated rice husk ash (QARHA) and the pre...

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Main Authors: Onyelowe, Kennedy C., Jalal, Fazal E., Onyia, Michael E., Onuoha, Ifeanyichukwu C., Alaneme, George U., Ikpa, Chidozie
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/18938/1/07.pdf
http://journalarticle.ukm.my/18938/
https://www.ukm.my/jkukm/volume-334-2021/
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spelling my-ukm.journal.189382022-07-13T06:59:23Z http://journalarticle.ukm.my/18938/ Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction Onyelowe, Kennedy C. Jalal, Fazal E. Onyia, Michael E. Onuoha, Ifeanyichukwu C. Alaneme, George U. Ikpa, Chidozie Artificial intelligence (AI) algorithms of adaptive neuro-fuzzy inference system or the adaptive network-based fuzzy inference system (ANFIS) has been deployed to predict the swelling potential (SP) of treated weak soil. The soil was treated with quicklime activated rice husk ash (QARHA) and the prediction efficiency was compared with the previous outcomes of this operation from literature. The need for effective utilization of construction materials to achieve sustainable designs and monitoring of the behavior of built environment is the motivation behind the deployment of artificial intelligence in geo-environmental research and field operations. The use of ANFIS is common in different fields of science and business to predict the best fits from several data points. The results of this modeling exercise conducted with 25 datasets from mixture experimental treatment of soft soil with QARHA has shown that ANFIS is a better tool compared to the individual algorithms of ANN and FL and even the other artificial intelligence tools like scheffe, ANOVA, regression and extreme vertices methods. With performance index of 88% and correlation of about 71% in the ANFIS testing and 17% and 99% respectively in the ANFIS training, ANFIS proved to be a more powerful tool in achieving a more sustainable material utilization in earthwork constructions, design and monitoring of geotechnical systems performance. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/18938/1/07.pdf Onyelowe, Kennedy C. and Jalal, Fazal E. and Onyia, Michael E. and Onuoha, Ifeanyichukwu C. and Alaneme, George U. and Ikpa, Chidozie (2021) Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction. Jurnal Kejuruteraan, 33 (4). pp. 845-852. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-334-2021/
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 Artificial intelligence (AI) algorithms of adaptive neuro-fuzzy inference system or the adaptive network-based fuzzy inference system (ANFIS) has been deployed to predict the swelling potential (SP) of treated weak soil. The soil was treated with quicklime activated rice husk ash (QARHA) and the prediction efficiency was compared with the previous outcomes of this operation from literature. The need for effective utilization of construction materials to achieve sustainable designs and monitoring of the behavior of built environment is the motivation behind the deployment of artificial intelligence in geo-environmental research and field operations. The use of ANFIS is common in different fields of science and business to predict the best fits from several data points. The results of this modeling exercise conducted with 25 datasets from mixture experimental treatment of soft soil with QARHA has shown that ANFIS is a better tool compared to the individual algorithms of ANN and FL and even the other artificial intelligence tools like scheffe, ANOVA, regression and extreme vertices methods. With performance index of 88% and correlation of about 71% in the ANFIS testing and 17% and 99% respectively in the ANFIS training, ANFIS proved to be a more powerful tool in achieving a more sustainable material utilization in earthwork constructions, design and monitoring of geotechnical systems performance.
format Article
author Onyelowe, Kennedy C.
Jalal, Fazal E.
Onyia, Michael E.
Onuoha, Ifeanyichukwu C.
Alaneme, George U.
Ikpa, Chidozie
spellingShingle Onyelowe, Kennedy C.
Jalal, Fazal E.
Onyia, Michael E.
Onuoha, Ifeanyichukwu C.
Alaneme, George U.
Ikpa, Chidozie
Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
author_facet Onyelowe, Kennedy C.
Jalal, Fazal E.
Onyia, Michael E.
Onuoha, Ifeanyichukwu C.
Alaneme, George U.
Ikpa, Chidozie
author_sort Onyelowe, Kennedy C.
title Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
title_short Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
title_full Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
title_fullStr Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
title_full_unstemmed Artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
title_sort artificial intelligence prediction model for swelling potential of soil and quicklime activated rice husk ash blend for sustainable construction
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2021
url http://journalarticle.ukm.my/18938/1/07.pdf
http://journalarticle.ukm.my/18938/
https://www.ukm.my/jkukm/volume-334-2021/
_version_ 1738511368190951424
score 13.211869

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