Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods
Empirically, soil compaction is an important aspect in the selection of materials for earth constructions. Due to time constraints and attention to completion resources, it is necessary to develop models to forecast compaction parameters (maximum dry unit weight (γdmax) and optimum moisture content...
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Bilijipub publisher
2023-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_169082_11cb4108629733b1f838892b0c4b44ca.pdf |
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| author | Arivalagan Pugazhendhi Ha Manh Bui |
| author_facet | Arivalagan Pugazhendhi Ha Manh Bui |
| author_sort | Arivalagan Pugazhendhi |
| collection | DOAJ |
| description | Empirically, soil compaction is an important aspect in the selection of materials for earth constructions. Due to time constraints and attention to completion resources, it is necessary to develop models to forecast compaction parameters (maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily measured index properties. The main purpose of this study is to scrutinize the applicability of using the hybrid adaptive neuro-fuzzy inference system (ANFIS) models for predicting the γdmax and ωopt related to the standard proctor compaction test of lateritic soils. Results present that both models have a reasonable performance in predicting the γdmax and ωopt with R2 larger than 0.9038 and 0.9692 for the training data, representing the acceptable correlation between measured and forecasted γdmax and ωopt. Regarding developed models, the ANFIS model optimized with whale optimization algorithm (WOA) has the best performance than imperialist competitive algorithm (ICA) model in both training and testing phases for predicting γdmax and ωopt. |
| format | Article |
| id | doaj-art-d1f015747a19436fb80ff9f6dc2b6990 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-d1f015747a19436fb80ff9f6dc2b69902025-02-12T08:47:02ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-03-0100201718510.22034/aeis.2023.385123.1077169082Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS MethodsArivalagan Pugazhendhi0Ha Manh Bui1Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, VietnamHo Chi Minh City University of Transport, Ho Chi Minh City, VietnamEmpirically, soil compaction is an important aspect in the selection of materials for earth constructions. Due to time constraints and attention to completion resources, it is necessary to develop models to forecast compaction parameters (maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily measured index properties. The main purpose of this study is to scrutinize the applicability of using the hybrid adaptive neuro-fuzzy inference system (ANFIS) models for predicting the γdmax and ωopt related to the standard proctor compaction test of lateritic soils. Results present that both models have a reasonable performance in predicting the γdmax and ωopt with R2 larger than 0.9038 and 0.9692 for the training data, representing the acceptable correlation between measured and forecasted γdmax and ωopt. Regarding developed models, the ANFIS model optimized with whale optimization algorithm (WOA) has the best performance than imperialist competitive algorithm (ICA) model in both training and testing phases for predicting γdmax and ωopt.https://aeis.bilijipub.com/article_169082_11cb4108629733b1f838892b0c4b44ca.pdflateritic soilsstandard proctor compaction testmaximum dry unit weightoptimum moisture contenthybrid adaptive neuro-fuzzy inference system |
| spellingShingle | Arivalagan Pugazhendhi Ha Manh Bui Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods Advances in Engineering and Intelligence Systems lateritic soils standard proctor compaction test maximum dry unit weight optimum moisture content hybrid adaptive neuro-fuzzy inference system |
| title | Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods |
| title_full | Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods |
| title_fullStr | Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods |
| title_full_unstemmed | Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods |
| title_short | Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods |
| title_sort | prediction the compaction properties of lateritic soils by hybrid anfis methods |
| topic | lateritic soils standard proctor compaction test maximum dry unit weight optimum moisture content hybrid adaptive neuro-fuzzy inference system |
| url | https://aeis.bilijipub.com/article_169082_11cb4108629733b1f838892b0c4b44ca.pdf |
| work_keys_str_mv | AT arivalaganpugazhendhi predictionthecompactionpropertiesoflateriticsoilsbyhybridanfismethods AT hamanhbui predictionthecompactionpropertiesoflateriticsoilsbyhybridanfismethods |