摘要
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 issues in geotechnical engineering. With the development of the social construction, this issue has become more and more prominent. Nowadays, China is in the phase of rapid development and its infrastructure construction has been building in full swing, which makes the issue of slope stability become more and more complicated. Especially the stability of expressway slope is of evident importance. Besides, its engineering geological environment is complicated and changeable and it＇s affected by many other factors. All of these reasons lead us to solve the issue of less accurate evaluation in highway slope stability urgently. In order to solve the difficulties brought about by the huge system of the evaluation of complicated highway slope stability, one needs to incorporate and combine every subject area to obtain more accurate evaluation methods about highway slope stability. In the paper, the fuzzy similar clustering model is introduced into the RBF neural network to build the model of fuzzy similar clustering RBF neural network of highway slope stability. And the model is applied to evaluate and predicate the stability of highway slope. Correlation studies show that the fuzzy clustering neural network model can evaluate the stability of highway slope reasonably and reliability. The good effect has been obtained through the cross-fusion of interdisciplinary field, which has further optimized the evaluation method of the slope stability.
出处
《公路》
北大核心
2015年第7期29-37,共9页
Highway
基金
湖南省教育厅科研基金资助重点项目
项目编号10A007
