【作者】 刘炎伏；

【导师】 杨子荣；

【作者基本信息】 辽宁工程技术大学 ， 地质工程， 2009， 硕士

【摘要】 本文以福建漳龙高速公路漳州到龙岩段97处边坡做为研究对象,通过实地调查,选取影响边坡稳定性的9项因子,运用模糊综合评判法、RBF人工神经网络法和多元不安定指数分析法三种统计方法,分别建立边坡稳定性影响因子与破坏程度间的关系,确定各影响因子对边坡的影响程度,预测边坡破坏发生的可能性,同时,针对边坡稳定性系数1这一判别准则并不能反映不同工程对边坡不同稳定性的要求,细划了边坡稳定性分类,以稳定系数1.2做为判别准则。通过对三种方法分析结果的综合评判,认为RBF人工神经网络分析法是最佳的分析模式;影响边坡稳定最重要的诱发因子是降雨量,潜在因子是岩体结构;提出了新的边坡稳定性分类:稳定、潜在不稳定和不稳定。更多还原

【Abstract】 In this paper, we choose 97 side slopes from Zhang Zhou to Long Yan high-speed road as the study of slope. Through on-the-spot investigation, selection influence side slope stable 9 factors, utilization fuzzy comprehensive judgment, RBF artificial nerve network method and multi-dimensional astatic index method of analysis three statistical methods, establishes the side slope stability influence factor and the destructiveness relations separately, determined that each influence factor to the side slope the influence, forecast the side slope destruction occurs the possibility, At the same time, the slope stability factor of 1 criteria do not reflect the different works on the stability of the requirements of different slope, so small slope damage classification designated to stabilize the coefficient of 1.2 as a criterion.Through three methods analysis result’s synthesis judgment, we thought that the RBF artificial neural networks analytic method is the best analysis pattern; The influence stability of slope most important suggestion factor is a rainfall amount, the latent factor is the rock mass structure; Proposed the new side slope stability classification is stable, latent unstable and unstable.更多还原