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.
Asadizadeh, M., & Majdi, A. (2019). Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability. International Journal of Mining and Geo-Engineering, 53(2), 133-142. doi: 10.22059/ijmge.2018.255209.594728
MLA
Mostafa Asadizadeh; abbas Majdi. "Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability". International Journal of Mining and Geo-Engineering, 53, 2, 2019, 133-142. doi: 10.22059/ijmge.2018.255209.594728
HARVARD
Asadizadeh, M., Majdi, A. (2019). 'Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability', International Journal of Mining and Geo-Engineering, 53(2), pp. 133-142. doi: 10.22059/ijmge.2018.255209.594728
VANCOUVER
Asadizadeh, M., Majdi, A. Developing new Adaptive Neuro-Fuzzy Inference System models to predict granular soil groutability. International Journal of Mining and Geo-Engineering, 2019; 53(2): 133-142. doi: 10.22059/ijmge.2018.255209.594728
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