Freeway Slope Stability Evaluation Based on Multi-Level Fuzzy Neural Network

Abstract:

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.

• Periodical: Advanced Materials Research (Volumes 430-432)
• Pages: 1700-1703
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.430-432.1700
• Online since: January 2012
• Authors: Yan Kai Wu, Xian Song Sang, Bin Niu
• Keywords: Fuzzy Artificial Neural Network, Mountainous Freeway, Slope Stability Evaluation

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