Modeling and Interpretation of Pressuremeter Test Results with Artificial Neural Networks

被引：6

作者：

Emami M. ^{[1
,2
]}

Yasrobi S.S. ^{[1
]}

机构：

[1] Department of Civil Engineering, Tarbiat Modares University, Tehran, Intersection of Jalale Ale Ahmad and Shahid Chamran High.

[2] Tehran, Unit 6, No. 16, 3th St., Kamali Boulvared, Ashrafi Esfeha.

来源：

Geotechnical and Geological Engineering | 2014年 / 32卷 / 02期

关键词：

Artificial neural network; Interpretation; Prediction; Pressuremeter; Sensitivity analysis;

D O I：

10.1007/s10706-013-9720-9

中图分类号：

学科分类号：

摘要：

In this paper, three types of artificial neural network (ANN) are employed to prediction and interpretation of pressuremeter test results. First, multi layer perceptron neural network is used. Then, neuro-fuzzy network is employed and finally radial basis function is applied. All applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance in two stages is determined. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models. © 2013 Springer Science+Business Media Dordrecht.

引用

页码：375 / 389

页数：14

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