Prediction of Compaction Characteristics of Granular Soils by Neural Networks

被引：0

作者：

Klos, Marzena ^{[1
]}

Waszczyszyn, Zenon ^{[1
]}

机构：

[1] Rzeszow Univ Technol, Chair Struct Mech, PL-35959 Rzeszow, Poland

来源：

ARTIFICIAL NEURAL NETWORKS-ICANN 2010, PT I | 2010年 / 6352卷

关键词：

Compaction characteristics; granular soils; neural networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

New experimental data discussed in [5] are used in the present paper. Application of the penalized error function, Principle Data Analysis and Bayesian criterion of Maximum Marginal Likelihood enabled design and training of numerically efficient small neural networks. They were applied for identification of two compaction characteristics, i.e. Optimum Water Content and Maximum Dry Density of granular soils.

引用

页码：42 / 45

页数：4

共 8 条

[1]

[Anonymous], 2009, ARTIFICIAL NEURAL NE

[2]

[Anonymous], 1933, ENG NEWS RECORD

[3]

[Anonymous], ALGORITHMS PATTERN R

[4]

[Anonymous], COMP AIDED MEC UNPUB

[5]

Demuth H., 2003, NEURAL NETWORK TOOLB

[6] On the identification of compaction characteristics by neuronets [J].

Najjar, YM ;

Basheer, IA ;

Naouss, WA .

COMPUTERS AND GEOTECHNICS, 1996, 18 (03) :167-187

[7] Artificial neural network prediction models for soil compaction and permeability [J].

Sinha S.K. ;

Wang M.C. .

Geotechnical and Geological Engineering, 2008, 26 (1) :47-64

[8]

Waszczyszyn Z, 2010, CISM COURSES LECT, V512, P237

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