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为研究青藏公路多年冻土区路基病害的分布规律,探究路基病害与其影响因素之间的相互关系,定量计算各因子对路基病害的影响权重,最终达到预测路基病害易发性概率的目的,以青藏公路西大滩至唐古拉山约470km范围内多年冻土段作为评价路段,选取体积含冰量、年平均地表温度、路堤高度、路堤走向、天然地表植被覆盖度、地表径流6个影响因子,通过已有图件地理信息提取、路基设计资料数据统计分析以及现场调查的方式,充分收集各因子的原始属性数据。同时,制作评价路段所在工程走廊内的径流分布图,通过图件与实地调查相结合的方式明确径流与公路相交的地点,并获取2014年青藏公路评价路段路基沉降面积的实测资料,以影响因子为自变量,以实测路基病害数据为因变量,引入Logistic模型进行回归分析,建立多年冻土区公路路基病害易发性的概率模型,计算评价路段内每个评价单元的路基病害易发性概率。研究结果表明:该概率模型回归结果的正确率为76.3%;易发性概率小于15%的路段占28.62%,易发性概率大于50%的路段占5.59%;易发性概率高的路段主要位于北麓河至风火山之间(K3064~K3066、K3070~K3071)、风火山至雅玛尔河之间(K3084~K3086)、沱沱河至开心岭之间(K3161~K3165)、开心岭至通天河之间(K3171、K3176~K3182、K3185)、雁石坪至唐古拉山之间(K3246)。
In order to study the distribution of subgrade defects in permafrost regions on the Qinghai-Tibet Highway, explore the relationship between subgrade disease and its influencing factors, and quantitatively calculate the influence weight of each factor on the subgrade disease, and finally achieve the purpose of predicting the probability of subgrade diseases susceptibility to Qinghai-Tibet Highway from the Xidatan to Tanggulashan about 470km permafrost section as the evaluation section, select the volume of ice, annual average surface temperature, embankment height, embankment, natural vegetation cover, surface runoff six factors, The original attribute data of each factor are fully collected through the extraction of the existing map pieces of geographic information, the statistical analysis of subgrade design data and the on-site investigation. At the same time, the distribution of runoff in the project corridor where the sections of the road are evaluated is made. The location where the runoff intersects with the road is determined through the combination of map and field survey. The measured data of subsidence area of the Qinghai-Tibet Highway section in 2014 are obtained, As independent variable, taking the measured subgrade disease data as the dependent variable, introducing Logistic model for regression analysis, establishing probability model of susceptibility to roadbed disease in permafrost regions, calculating the probability of subgrade disease susceptibility of each evaluation unit in the section . The results show that the correctness of the probability model is 76.3%, the percentage of vulnerable areas is less than 15%, the percentage of vulnerable areas is more than 50%, and the percentage of vulnerable areas is more than 50% Located between Beiluhe to Fenghuoshan (K3064 ~ K3066, K3070 ~ K3071), between Fenghuoshan and Yarmar (K3084 ~ K3086), between Tuotuohe and Kaixinling (K3161 ~ K3165) To Tongtianhe (K3171, K3176 ~ K3182, K3185), between Yan Geping and Tanggula (K3246).