基于智能算法的陕西省公路边坡稳定性评价

发布时间：2018-01-27 06:53

本文关键词： 公路边坡灾害人工智能支持向量机分区稳定性　出处：《长安大学》2015年博士论文　论文类型：学位论文

【摘要】：随着公路交通的快速发展,大量公路边坡问题不断出现,影响了当地的经济发展,研究边坡的稳定性对灾害的防治至关重要。由于边坡工程是复杂的系统工程,其中稳定性分析问题难以运用单一科学的理论与方法来有效解决,这已成为边坡工程研究者的共识。公路边坡地质灾害与众多影响因素有密切关系,且大部分情况下这种关系的高度非线性,使得传统稳定性分析方法存在一定的局限性。伴随科技的发展,人工智能成为探索公路边坡稳定程度的一种全新方法。根据目前信息化采集系统的情况,本文提出支持向量机方法应用于边坡稳定性分析中,以陕西省公路边坡地质灾害为分析对象,应用地理信息系统空间分析技术和支持向量机(SVM)方法,通过优选参数与建立稳定性分析模型,为分析边坡灾害探索一种新的方法。通过对陕西省的地形地貌、地质构造、岩土类型及其他因素进行调查分析,结合《陕西省日常养护管理系统》中的调查数据,阐述了导致陕西公路边坡灾害的最重要因素,确定其分析指标体系。结合陕西省区域环境特征,基于地理信息系统(Geographic Information System GIS)的区域评价指标叠加法,创建了以地震、降雨、地表切割深度、岩土类型为主要影响因素的公路边坡灾害分区评价模型,完成了各单因子图,并结合边坡灾害影响因素的关联度量化分析方法确定权重,获得陕西省公路边坡分区图,为分别确定各分区的稳定性智能分析模型奠定基础。依据分区的结果,分别对陕北、关中、陕南各自地域范围内的小样本数据,采用支持向量机建立灾害稳定性分析模型。运用研究区域的调查数据,将坡型、边坡坡度、最大坡高、风化程度、植被覆盖、边坡岩土体硬度,作为分析模型的输入向量,将公路边坡稳定情况作为输出向量。选择运用最广泛的径向基函数为核函数,并且选择了粒子群算法、遗传算法、网格搜索法三种参数优选的方法,通过不断寻优确定最佳惩罚因子C和核参数g,进行模型优化处理,最终得到高精度的智能模型。结果表明,网格法耗费时间比较少,精确度较差;而粒子群算法和遗传算法运算过程耗费时间较长,精确度较高。论文将分析模型结果,进行对比分析和取舍,最终使模型的精度达到95.8%,可靠性较高,可用于陕西省公路边坡稳定性分析。通过参数优选获得优良的SVM分析模型,稳定性和泛化性能均优良,经实测数据验证其精度较高,验证了基于智能算法—支持向量机公路边坡稳定性分析模型可以解决公路边坡系统的非线性特点,存在相对的优势与工程实际使用意义。基于MATLAB8.0平台,结合Microsoft Visual C++编辑器,采用libsvm工具箱进行扩展编程,开发了人工智能分析系统,对公路边坡稳定性进行智能化分析提供方便的操作平台。在倡导应用大数据进行公路评价的基础上,基于智能算法的公路边坡稳定性分析方法,可以科学地分析各种不同情况公路边坡的稳定性,给公路交通灾害的防治带来了参考作用和指导意义。
[Abstract]:With the rapid development of road traffic, a large number of highway slope problems continue to occur, affecting the local economic development, the research of slope stability on the prevention of disasters. The slope engineering is a complicated system engineering, the stability analysis problem by using the theory and method of single science to solve, which has become the research of slope engineering consensus. There is a close relationship between the factors of geological hazards of highway slope and many effects, and this relationship is highly nonlinear in most cases, the traditional method of stability analysis has some limitations. With the development of science and technology, artificial intelligence has become a new method of highway slope stability degree of exploration. According to the current situation of information acquisition system in this paper. The application of support vector machine method in slope stability analysis, slope in Shaanxi province highway geology disaster analysis As the application of geographic information system, spatial analysis technique and support vector machine (SVM) method, the optimum parameters and establish stability analysis model for analysis of slope disasters and explore a new method. According to the topography of Shaanxi Province, geological structure, rock and soil types and other factors were investigated and analyzed, combined with the survey data of Shaanxi "in daily maintenance management system > describes the most important factors, lead to slope disaster of highway in Shaanxi, the analysis index system of Shaanxi province. Combining the features of regional environment, based on geographic information system (Geographic Information System GIS) the regional evaluation index superposition method, created in the earthquake, rainfall, surface cutting depth, rock and soil for the partition evaluation model of the main factors affecting highway slope disaster, completed the single factor map, combined with the right to determine the slope disaster of quantitative analysis method Heavy, get Shaanxi province highway slope zoning map, respectively to determine the stability of each district intelligent analysis model of foundation. According to the zoning results of Northern Shaanxi, Guanzhong, Shaanxi small sample data in their respective geographical range, using support vector machine to establish stability analysis model of disaster. Using the survey data of the study area, the slope type the slope, slope, maximum slope height, weathering degree, vegetation cover, slope rock and soil hardness, as the input vector analysis model, the highway slope stability as the output vector. Select the most widely used radial basis function as the kernel function, and the particle swarm algorithm, genetic algorithm, grid search method three the optimization of the parameters, through continuous optimization to determine the optimal penalty factor and kernel parameter C g, model optimization, intelligent model with high accuracy is obtained. The results show that the grid method is more time consuming than Less and less accurate; and the particle swarm algorithm and genetic algorithm process takes a long time and high precision. The analysis results are compared with the model, the final choice, the precision of the model reached 95.8%, higher reliability, can be used to analyze the stability of highway slope in Shaanxi province. Excellent SVM analysis model through parameter optimization, stability and the generalization performance is excellent, the measured data to verify its accuracy, verify the analysis model can solve the nonlinear characteristic of highway slope system of slope stability of highway - support vector machine algorithm based on the existing advantages and engineering practical significance. Based on the MATLAB8.0 platform, combined with Microsoft Visual C++ editor, to extend programming using libsvm toolbox. Analysis system of artificial intelligence, intelligent analysis and convenient operation of highway slope stability As a platform. Based on the evaluation of highway advocate the use of big data on highway slope stability analysis method based on intelligent algorithm, can various slope stability analysis of highway science, bring the reference and guiding significance to the prevention and control of road traffic disaster.

【学位授予单位】：长安大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：U416.14

【相似文献】