Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability
Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature wa...
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| Format: | Article |
| Language: | English |
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University of Tehran
2019-08-01
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| Series: | International Journal of Mining and Geo-Engineering |
| Subjects: | |
| Online Access: | https://ijmge.ut.ac.ir/article_71416_1c99496a964758af98a46dff29c442ac.pdf |
| _version_ | 1828313419320655872 |
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| author | Mostafa Asadizadeh abbas Majdi |
| author_facet | Mostafa Asadizadeh abbas Majdi |
| author_sort | Mostafa Asadizadeh |
| collection | DOAJ |
| description | Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:cement ratio of the grout, relative density of the soil, grouting pressure, soil and grout particle size dimenstions namely D15 soil , D10 soil, d85 grout and d95 grout and percentage of the soil to pass through a 0.6 mm sieve. A accuracy of the ANFIS models was examined by comparing these models with the results of the experimental grout-ability tests. Sensitivity analysis showed that ratios of D15 soil / d85 grout and D10 soil / d95 grout were the most effective parameters on groutability of granular soil samples with cement-based grouts and the grouet water:cement ratio of the grout was determined as the least effective parameter. |
| first_indexed | 2024-04-13T16:30:35Z |
| format | Article |
| id | doaj.art-71d6910e21704e30a9024623c0e5dff6 |
| institution | Directory Open Access Journal |
| issn | 2345-6949 |
| language | English |
| last_indexed | 2024-04-13T16:30:35Z |
| publishDate | 2019-08-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | International Journal of Mining and Geo-Engineering |
| spelling | doaj.art-71d6910e21704e30a9024623c0e5dff62022-12-22T02:39:35ZengUniversity of TehranInternational Journal of Mining and Geo-Engineering2345-69492019-08-0153213314210.22059/ijmge.2018.255209.59472871416Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutabilityMostafa Asadizadeh0abbas Majdi1Hamedan University of TechnologyEditor-in-ChiefThree Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:cement ratio of the grout, relative density of the soil, grouting pressure, soil and grout particle size dimenstions namely D15 soil , D10 soil, d85 grout and d95 grout and percentage of the soil to pass through a 0.6 mm sieve. A accuracy of the ANFIS models was examined by comparing these models with the results of the experimental grout-ability tests. Sensitivity analysis showed that ratios of D15 soil / d85 grout and D10 soil / d95 grout were the most effective parameters on groutability of granular soil samples with cement-based grouts and the grouet water:cement ratio of the grout was determined as the least effective parameter.https://ijmge.ut.ac.ir/article_71416_1c99496a964758af98a46dff29c442ac.pdfgroutabilityanfisclustering algorithmgranular soil |
| spellingShingle | Mostafa Asadizadeh abbas Majdi Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability International Journal of Mining and Geo-Engineering groutability anfis clustering algorithm granular soil |
| title | Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability |
| title_full | Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability |
| title_fullStr | Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability |
| title_full_unstemmed | Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability |
| title_short | Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability |
| title_sort | developing new adaptive neuro fuzzy inference system models to predict granular soil groutability |
| topic | groutability anfis clustering algorithm granular soil |
| url | https://ijmge.ut.ac.ir/article_71416_1c99496a964758af98a46dff29c442ac.pdf |
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