Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system

Akbulut, Suat; Haşıloğlu, Abdulsamet; Pamukcu, Sibel

doi:10.1016/j.soildyn.2004.04.006

Cited by 63 publications

(26 citation statements)

References 15 publications

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“…Akbuluta et al (2004) used ANFIS for data generation of shear modulus and damping ratio in reinforced sands. Chau et al (2005) used ANFIS and ANN for comparison of flood forecasting models and reported that ANFIS obtained optimal results.…”

Section: Fmentioning

confidence: 99%

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam

Najjaran

2007

J. Mater. Civ. Eng.

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Section: Fmentioning

confidence: 99%

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam

Najjaran

2007

J. Mater. Civ. Eng.

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“…So, the determination of different dynamic properties {Maximum Shear Modulus (Gmax) and Minimum Damping Ratio (ξmin)} of synthetic reinforced soil is an imperative task in geotechnical earthquake engineering. Laboratory determination of dynamic properties is a tedious and time consuming task [6]. Recently, Akbulut et al [6] successfully used Adaptive Neuro-Fuzzy Inference (ANFIS) for determination of Gmax and ξmin of synthetic reinforced soil.…”

Section: Introductionmentioning

confidence: 99%

“…Laboratory determination of dynamic properties is a tedious and time consuming task [6]. Recently, Akbulut et al [6] successfully used Adaptive Neuro-Fuzzy Inference (ANFIS) for determination of Gmax and ξmin of synthetic reinforced soil. However, the developed ANFIS has low generalization capability.…”

Section: Introductionmentioning

confidence: 99%

“…It has been successfully used for solving different problems in engineering [9][10][11][12][13][14][15][16]. This article uses the database collected by [6]. The results of MARS have been compared with the models developed by [6].…”

Section: Introductionmentioning

confidence: 99%

“…This article uses the database collected by [6]. The results of MARS have been compared with the models developed by [6]. This study gives equations for prediction of Gmax and ξmin of synthetic reinforced soil.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Samui¹,

Kothari²

2012

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Abstract. This study uses Multivariate Adaptive Regression Spline (MARS) for determination Maximum Shear Modulus (Gmax) and Minimum Damping Ratio (ξmin) of synthetic reinforced soil. MARS employs confining pressure (, psi), rubber (r, %) and sand (s, %) as input variables. The output of the MARS is Gmax and min. The developed MARS gives equations for determination of Gmax and min. The results of MARS have been compared with the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) and multiple regression analysis method (MRM). A sensitivity analysis has been also carried out to determine the effect of each input variable on Gmax and min. This study shows that the developed MARS is a robust model for prediction of Gmax and min.

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Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System

Saltan

Terzi

2009

Intelligent and Soft Computing in Infrastructure Systems Engineering

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Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system

Cited by 63 publications

References 15 publications

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

A Multivariate Adaptive Regression Spline Approach for Prediction of Maximum Shear Modulus and Minimum Damping Ratio

Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System

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