Predicting groutability of granular soils using adaptive neuro-fuzzy inference system

Tekin, Erhan; Akbas, Sami O.

doi:10.1007/s00521-017-3140-3

Cited by 21 publications

(4 citation statements)

References 24 publications

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“…Hao et al [49] introduced the backpropagation neural network and the information diffusion method into grouting practice to predict grouting volume. Tekin and Akbas [50,51] constructed the artificial neural network model and the adaptive neurofuzzy inference system model to predict grouting volume. Liao et al [52] developed the radial basis function neural network to predict the grouting volume of infiltration grouting.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

Niu

Wang

et al. 2020

Geofluids

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The prediction of the grouting volume is a very important task in the grouting quality control. Because of the concealment and complexity of the karst curtain grouting engineering, there is little research on the prediction of the karst curtain grouting volume (KCGV), and the prediction is hindered by the practical problems of small samples, high dimensions, and nonlinearity. In the study, based on the basic idea of support vector machine (SVM), a multiparameter comprehensive intelligent prediction method of the KCGV is proposed, which overcomes the limitation of few sample data in practical engineering. This method takes the grouting construction conditions and the slurry conditions which control the slurry diffusion as the input parameters, which are the basic data which can be easily obtained in the field grouting process. This feature greatly improves the prediction accuracy and generalization performance of the method. The intelligent prediction method of the KCGV based on SVM is applied to a typical karst curtain grouting project. The mean absolute error of the prediction results is 3.47 L/m, and the mean absolute percentage error of the prediction results is 5.97%. The results show that the proposed prediction method has an excellent prediction effect on the KCGV and can provide practical and beneficial help for the field karst curtain grouting project.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

Niu

Wang

et al. 2020

Geofluids

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“…Recently, advanced artificial intelligence has successfully been adopted for solving complex modeling problems in various geotechnical engineering applications, i.e., predictions of soil strength [21][22][23], shear wave velocity [24], peak shear strength of fiber-reinforced soils [25], soil liquefaction [26,27], pile-soil interactions [28,29], and groutability of granular soils [30,31]. The main merit of these artificial intelligence approaches is the high prediction capability and the ability to deal with multivariate data [32,33].…”

Section: Introductionmentioning

confidence: 99%

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

et al. 2019

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In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg–Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R2) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.

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“…dams(Han et al 2020;El Bilali et al 2022), soil improvement(Tekin and Akbaş 2019;Saadat and Bayat 2022) …”

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confidence: 99%

Prediction of the Undrained Shear Strength of the Remolded Soil with Non-Linear Regression, Fuzzy Logic and Artificial Neural Network

Yünkül

KARAÇOR

Gürbüz

et al. 2023

Preprint

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The aim of this study is to predict the undrained shear strength (Cu) of the remolded soil samples and for this purpose, non-linear regression (NLR) analyses, fuzzy logic (FL) and artificial neural network (ANN) modelling were used to assess. Total 1306 undrained shear strength results of soil types of CH, CL, MH and ML from 230 different remolded soil test settings on 21 publications were collected while six different measurement devices were used by researchers. Although water content, plastic limit and liquid limit were used as input parameters for FL and ANN modelling, liquidity index or water content ratio were considered as input parameter for NLR analyses. In NLR analyses, 12 different regression equations were derived for prediction of Cu. Feed-Forward backpropagation and TANSIG transfer function were used for ANN modelling while Mamdani inference system was preferred with trapezoidal and triangular membership function for FL modelling. The experimental results of 914 tests for training of the ANN models, 196 for validation and 196 for testing were used. It was observed that the accuracy of the ANN and FL modellings were higher than NRL analyses. Furthermore, the simple and reliable regression equation was proposed for assessments of Cu values having higher coefficient of determination values (R2).

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Predicting groutability of granular soils using adaptive neuro-fuzzy inference system

Cited by 21 publications

References 24 publications

An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine

A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

Prediction of the Undrained Shear Strength of the Remolded Soil with Non-Linear Regression, Fuzzy Logic and Artificial Neural Network

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