基于模糊集理论的公路边坡稳定性评价与预测
发布时间：2018-10-09 14:43
【摘要】：本文在系统查阅、归纳和分析国内外文献资料基础上,结合现场监测结果,对公路边坡稳定性影响因素进行分析,选取影响边坡稳定的主要因素作为评价指标。结合模糊聚类理论、模糊模式识别和模糊优选理论,建立公路边坡稳定性模糊相似聚类模型。在此基础上建立公路边坡稳定性模糊相似聚类RBF神经网络模型。通过研究,得出了如下主要研究成果：1.通过查阅文献资料和现场调研,结合湖南公路边坡滑坡灾害现状、地形地貌特征以及工程地质环境,对影响公路边坡稳定性的主要因素进行分析、归纳和分类,将岩土体容重、粘聚力、内摩擦角、坡高、坡角、孔隙水压力比作为边坡稳定性分析的模糊评价指标。2.采用二元对比排序法计算影响公路边坡稳定性评价的六个主要指标所占权重,根据加权模糊聚类算法,建立了公路边坡稳定性模糊聚类预测模型,对现有模糊聚类迭代模型进行了相应的改进。3.结合模糊聚类理论、模糊模式识别以及模糊优选理论,建立了公路边坡稳定性模糊相似聚类模型,进一步优化模糊聚类算法,提高了计算效率和评价的准确度。针对不同的相似聚类水平β进行对比研究,得出了最佳相似聚类水平夕为0.8。4.将模糊相似聚类模型引入到RBF神经网络中,建立模糊相似聚类神经网络模型。结合工程实例分析,该模型能可靠地应用于公路边坡稳定性评价与预测,进一步优化了边坡稳定性的评价方法。
[Abstract]:On the basis of systematic reference, induction and analysis of domestic and foreign literature and data, combined with site monitoring results, this paper analyzes the influencing factors of highway slope stability, and selects the main factors affecting slope stability as the evaluation index. Combined with fuzzy clustering theory, fuzzy pattern recognition and fuzzy optimal selection theory, fuzzy similar clustering model of highway slope stability is established. On this basis, the fuzzy similar clustering RBF neural network model of highway slope stability is established. Through research, the following main research results: 1. 1. By referring to the literature and field investigation, combining with the present situation of landslide disaster of highway slope in Hunan Province, the characteristics of landform and geomorphology and the engineering geological environment, the main factors affecting the stability of highway slope are analyzed, summarized and classified, and the bulk density of rock and soil is classified. Cohesion, internal friction angle, slope height, slope angle and pore-water pressure ratio are the fuzzy evaluation indexes of slope stability analysis. The weight of six main indexes affecting the evaluation of highway slope stability is calculated by using the binary contrast ranking method. According to the weighted fuzzy clustering algorithm, the fuzzy cluster prediction model of highway slope stability is established. The existing fuzzy clustering iterative model is improved. 3. Combined with fuzzy clustering theory, fuzzy pattern recognition and fuzzy optimal selection theory, the fuzzy similar clustering model of highway slope stability is established, and the fuzzy clustering algorithm is further optimized to improve the calculation efficiency and evaluation accuracy. According to the comparative study of different similarity clustering levels 尾, the best similarity clustering level is 0.8.4. The fuzzy similar clustering model is introduced into RBF neural network and the fuzzy similar clustering neural network model is established. The model can be reliably applied to the evaluation and prediction of highway slope stability, and the evaluation method of slope stability is further optimized.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U416.14
本文编号：2259723
[Abstract]:On the basis of systematic reference, induction and analysis of domestic and foreign literature and data, combined with site monitoring results, this paper analyzes the influencing factors of highway slope stability, and selects the main factors affecting slope stability as the evaluation index. Combined with fuzzy clustering theory, fuzzy pattern recognition and fuzzy optimal selection theory, fuzzy similar clustering model of highway slope stability is established. On this basis, the fuzzy similar clustering RBF neural network model of highway slope stability is established. Through research, the following main research results: 1. 1. By referring to the literature and field investigation, combining with the present situation of landslide disaster of highway slope in Hunan Province, the characteristics of landform and geomorphology and the engineering geological environment, the main factors affecting the stability of highway slope are analyzed, summarized and classified, and the bulk density of rock and soil is classified. Cohesion, internal friction angle, slope height, slope angle and pore-water pressure ratio are the fuzzy evaluation indexes of slope stability analysis. The weight of six main indexes affecting the evaluation of highway slope stability is calculated by using the binary contrast ranking method. According to the weighted fuzzy clustering algorithm, the fuzzy cluster prediction model of highway slope stability is established. The existing fuzzy clustering iterative model is improved. 3. Combined with fuzzy clustering theory, fuzzy pattern recognition and fuzzy optimal selection theory, the fuzzy similar clustering model of highway slope stability is established, and the fuzzy clustering algorithm is further optimized to improve the calculation efficiency and evaluation accuracy. According to the comparative study of different similarity clustering levels 尾, the best similarity clustering level is 0.8.4. The fuzzy similar clustering model is introduced into RBF neural network and the fuzzy similar clustering neural network model is established. The model can be reliably applied to the evaluation and prediction of highway slope stability, and the evaluation method of slope stability is further optimized.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U416.14
【参考文献】
相关期刊论文 前1条
1 罗国煜,刘松玉,杨卫东;区域稳定性优势面分析理论与方法[J];岩土工程学报;1992年06期
，本文编号：2259723
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