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

被引：1

作者：

Umu, Seyfettin U. ^{[1
]}

Onur, Mehmet I. ^{[2
]}

Okur, Volkan ^{[3
]}

Tuncan, Mustafa ^{[2
]}

Tuncan, Ahmet ^{[2
]}

机构：

[1] Anadolu Univ, Transportat Vocat Sch, Eskisehir, Turkey

[2] Anadolu Univ, Dept Civil Engn, Eskisehir, Turkey

[3] Eskisehir Osmangazi Univ, Dept Civil Engn, Eskisehir, Turkey

来源：

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2016年 / 41卷 / 04期

关键词：

Toyoura sand; Resonant column test; Artificial neural networks; Fuzzy expert system; Regression analysis; NEURAL-NETWORKS; SHEAR MODULUS; TENSILE-STRENGTH; DAMPING RATIO; PREDICTION; BEHAVIOR;

D O I：

10.1007/s13369-015-1883-7

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页码：1363 / 1373

页数：11

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