Reliability Evaluation of Dynamic Characteristics of Clean Sand Soils Based on Soft Computing Methods

被引：0
作者：
Seyfettin U. Umu
Mehmet I. Onur
Volkan Okur
Mustafa Tuncan
Ahmet Tuncan
机构：
[1] Anadolu University,Transportation Vocational School
[2] Anadolu University,Department of Civil Engineering
[3] Eskisehir Osmangazi University,Department of Civil Engineering
来源：
Arabian Journal for Science and Engineering | 2016年 / 41卷
关键词：
Toyoura sand; Resonant column test; Artificial neural networks; Fuzzy expert system; Regression analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study was conducted to estimate the dynamic characteristics of clean sand under low strains using fuzzy expert systems and neural network approximations. A series of resonant column tests were conducted on clean sand specimens to create a large database. The effects of various factors, such as effective pressure, saturation, void ratio and shear strain levels, were simulated using fuzzy expert systems and neural networks. The neuro-fuzzy inference method was employed to predict the initial shear modulus of clean sand samples as a substitute for time-consuming laboratory testing. Additionally, the maximum shear modulus results were compared with the existing empirical relationships. From these observations, it can be observed that certain relationships significantly underestimate the initial shear modulus. Simple empirical relationships to estimate the initial shear modulus were formulated. It is concluded that neuro-fuzzy-based models provide useful guidelines for the preliminary estimation of the dynamic shear modulus for clean sand soils.
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页码：1363 / 1373
页数：10
相关论文
共 42 条
[1]  
Youn J.(2008)Measurement of small-strain shear modulus gmax of dry and saturated sands by bender element, resonant column and torsional shear tests Can. Geotech. J. 45 1426-1438
[2]  
Choo Y.(2012)On the compression behaviour of reconstituted soils Soils Found. 52 668-681
[3]  
Kim D.(2010)Postcyclic reconsolidation strains in low-plastic Fraser river silt due to dissipation of excess pore-water pressures J. Geotech. Geoenviron. Eng. 136 1347-1357
[4]  
Shipton B.(2013)Development of an inverse analysis framework for extracting dynamic soil behavior and pore pressure response from downhole array measurements Int. J. Numer. Anal. Methods Geomech. 37 1867-1890
[5]  
Coop M.R.(2004)Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system Soil Dyn. Earthq. Eng. 24 805-814
[6]  
Wijewickreme D.(1996)Characterization of granular material by low strain dynamic excitation and ANN Geotech. Spec. Publ. ASTM-ASCE 58 1134-1148
[7]  
Sanin M.(2005)Artificial neural network for stress–strain behavior of sandy soils: knowledge based verification Comput. Geotech. 32 377-386
[8]  
Groholski D.R.(2003)Neurofuzzy mapping of CPT values into soil dynamic properties Soil Dyn. Earthq. Eng. 23 473-482
[9]  
Hashash Y.M.A.(2009)Modelling damping ratio and shear modulus of sand–mica mixtures using neural networks Eng. Geol. 104 31-40
[10]  
Akbulut S.(2011)Triaxial behavior of sand–mica mixtures using genetic programming Expert Syst. Appl. 38 10358-10367
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