ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils
Civil engineering faces significant challenges from expansive soils, which can lead to structural damage. This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. Two ANFIS models were deve...
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| Format: | Article |
| Language: | English |
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Pouyan Press
2024-10-01
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| Series: | Journal of Soft Computing in Civil Engineering |
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| Online Access: | https://www.jsoftcivil.com/article_196441_883cb928ea9ae1492825882d286ff2fe.pdf |
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| author | Mehdi Hashemi Jokar Ali Heidaripanah |
| author_facet | Mehdi Hashemi Jokar Ali Heidaripanah |
| author_sort | Mehdi Hashemi Jokar |
| collection | DOAJ |
| description | Civil engineering faces significant challenges from expansive soils, which can lead to structural damage. This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. Two ANFIS models were developed, namely the FIS1S model using subtractive clustering and the FIS2S model utilizing the FCM algorithm. Due to the MATLAB graphical user interface's limitation on the number of membership functions, the coding approach was employed to develop the ANFIS models for optimal prediction accuracy and problem-solving time. So, two programs were created to determine the optimal influence radius for the FIS1S model and the number of membership functions for the FIS2S model to achieve the highest prediction accuracy. The ANFIS models have demonstrated their highest predictive ability in predicting swelling percentage, thanks to the optimization of membership functions and cluster centers. The developed programs also showed excellent performance and can be potentially applied to optimize subtractive clustering and FCM models in accurately modeling various engineering aspects. |
| format | Article |
| id | doaj-art-43313b45e4694e11bad3bae3a5b29ddc |
| institution | DOAJ |
| issn | 2588-2872 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-43313b45e4694e11bad3bae3a5b29ddc2025-08-20T03:12:26ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722024-10-018414115910.22115/scce.2024.408595.1691196441ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive SoilsMehdi Hashemi Jokar0Ali Heidaripanah1Graduate University of Advanced Technology, Kerman, IranAssistant Professor, Graduate University of Advanced Technology, Kerman, IranCivil engineering faces significant challenges from expansive soils, which can lead to structural damage. This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. Two ANFIS models were developed, namely the FIS1S model using subtractive clustering and the FIS2S model utilizing the FCM algorithm. Due to the MATLAB graphical user interface's limitation on the number of membership functions, the coding approach was employed to develop the ANFIS models for optimal prediction accuracy and problem-solving time. So, two programs were created to determine the optimal influence radius for the FIS1S model and the number of membership functions for the FIS2S model to achieve the highest prediction accuracy. The ANFIS models have demonstrated their highest predictive ability in predicting swelling percentage, thanks to the optimization of membership functions and cluster centers. The developed programs also showed excellent performance and can be potentially applied to optimize subtractive clustering and FCM models in accurately modeling various engineering aspects.https://www.jsoftcivil.com/article_196441_883cb928ea9ae1492825882d286ff2fe.pdfexpansive soilsswellingsubtractive clusteringfuzzy c-mean clustering (fcm)sensitivity analysis |
| spellingShingle | Mehdi Hashemi Jokar Ali Heidaripanah ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils Journal of Soft Computing in Civil Engineering expansive soils swelling subtractive clustering fuzzy c-mean clustering (fcm) sensitivity analysis |
| title | ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils |
| title_full | ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils |
| title_fullStr | ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils |
| title_full_unstemmed | ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils |
| title_short | ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils |
| title_sort | anfis models with subtractive clustering and fuzzy c mean clustering techniques for predicting swelling percentage of expansive soils |
| topic | expansive soils swelling subtractive clustering fuzzy c-mean clustering (fcm) sensitivity analysis |
| url | https://www.jsoftcivil.com/article_196441_883cb928ea9ae1492825882d286ff2fe.pdf |
| work_keys_str_mv | AT mehdihashemijokar anfismodelswithsubtractiveclusteringandfuzzycmeanclusteringtechniquesforpredictingswellingpercentageofexpansivesoils AT aliheidaripanah anfismodelswithsubtractiveclusteringandfuzzycmeanclusteringtechniquesforpredictingswellingpercentageofexpansivesoils |