Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods
Soil shear strength is an essential engineering characteristic used in designing and evaluating geotechnical structures. In this study, we intend to analyse and compare the performance of the Genetic Algorithm - Adaptive Network-based Fuzzy Inference System (GANFIS) and Artificial Neural Networks (A...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22180/ http://journalarticle.ukm.my/22180/1/kjt_7.pdf |
| _version_ | 1848815535229239296 |
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| author | Rufaizal Che Mamat, Sri Atmaja P. Rosyidi, Azuin Ramli, |
| author_facet | Rufaizal Che Mamat, Sri Atmaja P. Rosyidi, Azuin Ramli, |
| author_sort | Rufaizal Che Mamat, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Soil shear strength is an essential engineering characteristic used in designing and evaluating geotechnical structures. In this study, we intend to analyse and compare the performance of the Genetic Algorithm - Adaptive Network-based Fuzzy Inference System (GANFIS) and Artificial Neural Networks (ANN) in predicting the strength of soft clay. Case studies of 144 soft clay soil samples from Sarang Buaya, Semerah, Malaysia, were utilised to generate training and testing datasets for developing and validating models. RMSE and R have been employed to validate and compare the models. The GANFIS has the highest prediction capability (RMSE=0.042 and R=0.850), while the ANN has the lowest (RMSE=0.065 and R=0.49). From a comparison of the two models, it can be stated that GANFIS is the most promising technique for predicting the strength of soft clay. |
| first_indexed | 2025-11-15T00:51:31Z |
| format | Article |
| id | oai:generic.eprints.org:22180 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:51:31Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:221802023-09-13T06:34:19Z http://journalarticle.ukm.my/22180/ Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods Rufaizal Che Mamat, Sri Atmaja P. Rosyidi, Azuin Ramli, Soil shear strength is an essential engineering characteristic used in designing and evaluating geotechnical structures. In this study, we intend to analyse and compare the performance of the Genetic Algorithm - Adaptive Network-based Fuzzy Inference System (GANFIS) and Artificial Neural Networks (ANN) in predicting the strength of soft clay. Case studies of 144 soft clay soil samples from Sarang Buaya, Semerah, Malaysia, were utilised to generate training and testing datasets for developing and validating models. RMSE and R have been employed to validate and compare the models. The GANFIS has the highest prediction capability (RMSE=0.042 and R=0.850), while the ANN has the lowest (RMSE=0.065 and R=0.49). From a comparison of the two models, it can be stated that GANFIS is the most promising technique for predicting the strength of soft clay. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22180/1/kjt_7.pdf Rufaizal Che Mamat, and Sri Atmaja P. Rosyidi, and Azuin Ramli, (2023) Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods. Jurnal Kejuruteraan, 35 (3). pp. 597-605. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3503-2023/ |
| spellingShingle | Rufaizal Che Mamat, Sri Atmaja P. Rosyidi, Azuin Ramli, Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title | Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title_full | Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title_fullStr | Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title_full_unstemmed | Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title_short | Shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| title_sort | shear strength prediction of treated soft clay with sugarcane bagasse ash using artificial intelligence methods |
| url | http://journalarticle.ukm.my/22180/ http://journalarticle.ukm.my/22180/ http://journalarticle.ukm.my/22180/1/kjt_7.pdf |