Abstract
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.
Highlights
Jet grouting is one of the best available methods of ground improvement
Tinoco et al [4] utilized an advanced statistics analysis that is usually known as Data Mining (DM) techniques in their study to establish a new procedure to predict the Uniaxial Compressive strength (UCS) of the Soilcrete mixtures based on Support Vector Machines (SVM) algorithm
This study provides the exact breakdown of success estimation percentages for chosen functions
Summary
Jet grouting is one of the best available methods of ground improvement. Jet grouting can provide the higheststrength treated ground in comparison with other methods of ground improvement. Tinoco et al [4] utilized an advanced statistics analysis that is usually known as Data Mining (DM) techniques in their study to establish a new procedure to predict the Uniaxial Compressive strength (UCS) of the Soilcrete mixtures based on Support Vector Machines (SVM) algorithm. It should be mentioned that all the previous studies were mostly focused on statistical procedures and results whereas this study used the actual numerical data of different projects and introduced a certain new numerical calculative method Using this approach it is possible to anticipate the diameter of the Soilcrete, produce practical numerical outcomes and provide a prior attitude before the beginning of the jet grouting process. This study provides the exact breakdown of success estimation percentages for chosen functions
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