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A Novel Approach in Estimation of the Soilcrete Column’s Diameter and Optimization of the High Pressure Jet Grouting Using Adaptive Neuro Fuzzy Inference System (ANFIS)

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=48998#.VJeK-cCAM4

For achieving optimized jet grout parameters and W/C ratio it is concluded to set trial tests in constant local soil as the conclusion depends on local soil and presence of the extensive range of the effective parameters. Considering the benefits, due to abundance of the involved variables and the intrinsic geological complexity, this system follows a great expense in the trial and implementation phases. Utilizing the soft computing methods, this paper proposes a new approach to reduce or to eliminate the cost of the trial phase. Therefore, the Adaptive Neuro Fuzzy Inference System (ANFIS) was utilized to study the possibility of anticipating the diameter of the jet grout (Soilcrete) columns on the trial phase based on the Trial and Error procedure. Data were collected from several projects and formed three sets of data. Consequently, parameters were held constant (as input) and the diameters of the Soilcrete columns were recorded (as output). To increase the precision, aforementioned data sets were combined and ten different data sets were created and studied, with all the results being assessed in two different approaches. Accordingly, Gaussian Function results in a huge number of precise and acceptable outcomes among available functions. Based on the measurements, Gaussian Function achieves the values of the R which are frequently more than 0.8 and lower values of the RMSE. Therefore, utilizing Gaussian Function, mainly a congruent relation between the R and RMSE is experienced and it leads to close proximity of the actual and predicted values of the Soilcrete diameter.
Cite this paper
Ehsanzadeh, B. and Ahangari, K. (2014) A Novel Approach in Estimation of the Soilcrete Column’s Diameter and Optimization of the High Pressure Jet Grouting Using Adaptive Neuro Fuzzy Inference System (ANFIS). Open Journal of Geology, 4, 386-398. doi: 10.4236/ojg.2014.48030. 
 

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