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边坡稳定性分析一直以来都是岩土工程中的核心问题之一,随着社会建设的不断发展,这一问题也变得越来越突出。当今,我国正处在快速发展阶段,各项基础设施建设正在如火如荼地开展,使得边坡稳定性问题变得越来越复杂。特别是高速公路的边坡稳定性,其重要性不言而喻。其工程地质环境复杂多变并受到多种因素的影响,致使人们亟需解决公路边坡稳定性中不太准确的评价问题。为了解决复杂公路边坡稳定性评价这一庞大的系统带来的困难,需要将各学科领域进行交叉融合,以期获得较准确的公路边坡稳定性评价方法。文中将模糊相似聚类模型引入到RBF神经网络中,建立公路边坡稳定性模糊相似聚类RBF神经网络模型,并将该模型应用于公路边坡稳定性评价与预测。研究表明,模糊相似聚类神经网络模型能够合理、可靠地评价公路边坡稳定性。通过各学科领域的交叉融合取得了良好的效果,进一步优化了边坡稳定性评价方法。
The analysis of slope stability has always been one of the core problems in geotechnical engineering. With the continuous development of social construction, this problem has become more and more prominent. Nowadays, our country is in a period of rapid development. All infrastructure construction is in full swing. As a result, the stability of the slope is getting more and more complicated. In particular, the slope stability of the highway, its importance is self-evident. The engineering geological environment is complex and changeable and affected by many factors, which makes it urgently needed to solve the less accurate evaluation problem in highway slope stability. In order to solve the problem caused by the huge system of complex highway slope stability evaluation, it is necessary to cross-integrate the various subject areas in order to obtain a more accurate method of slope stability assessment. In this paper, the fuzzy similar cluster model is introduced into the RBF neural network, and a fuzzy similarity cluster RBF neural network model of highway slope stability is established. The model is applied to the highway slope stability evaluation and prediction. The research shows that the fuzzy similar clustering neural network model can reasonably and reliably evaluate the slope stability of highway. Through the cross-integration of various disciplines and achieved good results, to further optimize the slope stability evaluation method.