پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,230 |
| تعداد مقالات | 67,755 |
| تعداد مشاهده مقاله | 115,099,906 |
| تعداد دریافت فایل اصل مقاله | 89,852,228 |
Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability | ||
| International Journal of Mining and Geo-Engineering | ||
| مقاله 5، دوره 53، شماره 2، اسفند 2019، صفحه 133-142 اصل مقاله (1020.59 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2018.255209.594728 | ||
| نویسندگان | ||
| Mostafa Asadizadeh* 1؛ abbas Majdi2 | ||
| 1Hamedan University of Technology | ||
| 2Editor-in-Chief | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Groutability؛ ANFIS؛ Clustering Algorithm؛ Granular soil | ||
|
آمار تعداد مشاهده مقاله: 515 تعداد دریافت فایل اصل مقاله: 590 |
||