A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY

XUE Xinhua; YAO Xiaodong

Volume 15 Issue 1

Apr. 2011

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XUE Xinhua, YAO Xiaodong. 2007: A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY. JOURNAL OF ENGINEERING GEOLOGY, 15(1): 77-82.

Citation:

XUE Xinhua, YAO Xiaodong. 2007: A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY. JOURNAL OF ENGINEERING GEOLOGY, 15(1): 77-82.

XUE Xinhua, YAO Xiaodong. 2007: A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY. JOURNAL OF ENGINEERING GEOLOGY, 15(1): 77-82.

Citation:

XUE Xinhua, YAO Xiaodong. 2007: A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY. JOURNAL OF ENGINEERING GEOLOGY, 15(1): 77-82.

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A FUZZY NEURAL NETWORK MODEL FOR PREDICTING SLOPE STABILITY

XUE Xinhua¹,
YAO Xiaodong²

1.
Institute of Geotechnical Engineering, Zhejiang University, Hangzhou 310027;
2.
Zhong Tian Construction Group, Hangzhou 310008

Funds:

Received Date: 2006-04-12
Rev Recd Date: 2006-07-06
Publish Date: 2007-01-22

Abstract

Abstract

Accordingto the fuzzy characteristics of slope stability,a fuzzy neural network model is presented to predict slope stability.In this model,theintention of acquiringthe initialfuzzy rule sets can be achieved only by using the desired input-output data pairs.Then,employing neural networks learning techniques,the fuzzy logic rules,input-output fuzzy membership functions and weights in network can be easily tuned.So the rule matching is reduced.The velocity of inference is accelerated.Adaptabilityof the system is greatly improved.At last,the collected data of slope stability are adapted to train and test the model.The forecasted results show that the proposed method is feasible and effective in predicting slope stability.
- Hybridtraining algorithm,
- Slope stability,
- Neural network,
- Fuzziness

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References(1)

References

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