Prediction of the unconfined compressive strength of compacted granular soils by using inference systems

Kalkan, Ekrem; Akbulut, Suat; Tortum, Ahmet; Çelik, Samet

doi:10.1007/s00254-008-1645-x

Cited by 54 publications

(20 citation statements)

References 28 publications

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“…2 to fit or describe investigated stressstrain data, many researchers have found the inferior of twoparameter hyperbolic model 14 . In order to improve the twoparameter hyperbolic model, we propose following formula to describe the stress-strain curve: (3) In Eq. 2 and Eq.…”

Section: Resultsmentioning

confidence: 99%

“…Te work team of Liu 1 conducted the assessment of unconfined compressive strength of cement stabilized marine clay. Effect of suction on unconfined compressive strength in partly saturated soils was discussed 2 and the unconfined compressive strength of compacted granular soils by using inference systems was discussed 3,4 . In some case, soil foundation or slope may experience freezing-thawing circle (FTC) process due to nature season change, day and night temperature difference, or artificial freezing engineering, which would make big difference to unconfined compressive strength of soil 5,6 .…”

Section: Introductionmentioning

confidence: 99%

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Experimental and Mathematical Investigations on Unconfined Compressive Behaviour of Costal Soft Soil under Complicated Freezing Processes

Wang

Zhang

et al. 2016

Polish Maritime Research

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental and Mathematical Investigations on Unconfined Compressive Behaviour of Costal Soft Soil under Complicated Freezing Processes

Wang

Zhang

et al. 2016

Polish Maritime Research

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show abstract

“…Khalilmoghadam et al (2009) investigated the potential use of three different neural network structures (i.e., generalized feed-forward, MLP, and modular feedforward networks) for estimating the surface soil shear strength and reported that use of the MLP neural network resulted in higher correlation coefficients as compared with the generalized feed-forward and modular neural network. Kalkan et al (2009) indicated that ANN models can be developed as useful tools for predicting the unconfined compressive strength of compacted granular soils. Anagu et al (2009) concluded that ANN is a versatile tool for the estimation of heavy metal sorption from basic soil properties.…”

Section: Ann Modelmentioning

confidence: 99%

“…Soft computing techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have attracted greater interest recently for use in the prediction of soil properties (Minasny et al 2004;Azamathulla et al 2009;Kalkan et al 2009;Huang et al 2010;Dai et al 2011). These methods, in comparison to the traditional regression soil property prediction functions and PTFs, do not require a priori regression models to relate input and output data (Schaap and Leij 1998).…”

Section: Introductionmentioning

confidence: 99%

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Besalatpour

Hajabbasi

Ayoubi

et al. 2012

Soil Science and Plant Nutrition

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Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.

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“…Another appropriate method in the prediction of liquefaction potential is the neuro-fuzzy system, which is a combination of neural networks and fuzzy logic to determine parameters of fuzzy systems using neural network training algorithm [32]. Fuzzy systems have successful application in geotechnical problems such as prediction of uncon ned compressive strength of compacted granular soils [33], prediction of foundation response [34], swelling potential of compacted soil [35], estimation of sand permeability [36], and evaluation of liquefaction potential [37]. Other neurofuzzy applications were reported by Cabalar et al [37].…”

Section: Introductionmentioning

confidence: 99%

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

2017

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Abstract. In recent years, controlled blasting has turned into an e cient method for evaluation of soil liquefaction on a real scale and of ground improvement techniques. Predicting blast-induced soil liquefaction using collected information can be an e ective step in the study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, rst, a multi-layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, a neuro-fuzzy network, ANFIS, was used for modeling. Di erent ANFIS models are created using Grid Partitioning (GP), subtractive clustering (SCM), and Fuzzy C-Means clustering (FCM). Minimum error is obtained using FCM at about 0.081. Finally, Radial Basis Function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables, including ne-content, relative density, e ective overburden pressure, and SPT value, are considered as input components, and residual pore pressure ratio, Ru, was used as the only output component for designing prediction models. In the next stage, the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested, and its results reveal that 0 v0 and SPT are the most e ective factors for determining Ru.

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Prediction of the unconfined compressive strength of compacted granular soils by using inference systems

Cited by 54 publications

References 28 publications

Experimental and Mathematical Investigations on Unconfined Compressive Behaviour of Costal Soft Soil under Complicated Freezing Processes

Experimental and Mathematical Investigations on Unconfined Compressive Behaviour of Costal Soft Soil under Complicated Freezing Processes

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

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