许强, 李骅锦, 何雨森, 亓星, 罗双. 2017: 文家沟泥石流治理工程效果的定量分析评价. 工程地质学报, 25(4): 1046-1056. DOI: 10.13544/j.cnki.jeg.2017.04.019
    引用本文: 许强, 李骅锦, 何雨森, 亓星, 罗双. 2017: 文家沟泥石流治理工程效果的定量分析评价. 工程地质学报, 25(4): 1046-1056. DOI: 10.13544/j.cnki.jeg.2017.04.019
    XU Qiang, LI Huajin, HE Yusen, QI Xing, LUO Shuang. 2017: QUANTITATIVE EVALUATION OF ENGINEERING TREATMENTS FOR PREVENTION OF DEBRIS FLOW AT WENJIA GULLY. JOURNAL OF ENGINEERING GEOLOGY, 25(4): 1046-1056. DOI: 10.13544/j.cnki.jeg.2017.04.019
    Citation: XU Qiang, LI Huajin, HE Yusen, QI Xing, LUO Shuang. 2017: QUANTITATIVE EVALUATION OF ENGINEERING TREATMENTS FOR PREVENTION OF DEBRIS FLOW AT WENJIA GULLY. JOURNAL OF ENGINEERING GEOLOGY, 25(4): 1046-1056. DOI: 10.13544/j.cnki.jeg.2017.04.019

    文家沟泥石流治理工程效果的定量分析评价

    QUANTITATIVE EVALUATION OF ENGINEERING TREATMENTS FOR PREVENTION OF DEBRIS FLOW AT WENJIA GULLY

    • 摘要: 汶川地震后文家沟频繁暴发泥石流,给当地居民带了巨大的财产损失并造成了人员伤亡。为了防范泥石流带来的危害,文家沟治理工程采用了"上游水砂分离、中游固底护坡、下游拦挡停淤"的总体思路并完成治理。在治理工程经历了5个雨季的考验之后,本文基于生存分析模型以及Bootstrap方法和ELM,从定量研究的角度对治理工程的效果进行分析评价。生存分析模型结果表明治理前生存概率降低至0%时,治理后仍为55.6%;治理后暴发泥石流的最大小时降雨量为治理前的2.571倍,降雨总量为治理前的1.232倍,降雨历时为治理前的5.435倍;模型结果体现了治理工程泥石流防范的重要作用。Bootstrap方法以及ELM模型结果表明,在假设没有治理工程情况下,2011~2015年的10次降雨事件暴发泥石流概率为100%,远高于真实情况的30%;且预测的泥石流冲出量远远大于真实的冲出量;模型结果体现了治理工程防灾减灾的重要性。

       

      Abstract: After Wenchuan Earthquake in China, debris flows as a kind of geohazard occur frequently in Wenjia Gully. The debris flows have already cost millions of property and casualty losses to the local community. For the purpose of preventing debris flows, geological engineering treatments have been implemented in multiple locations of Wenjia Gully. The adopted preventive measures include water and sediment separation upstream, reinforce bottom of channel reinforcement and slope protection midstream, damming against and silting downstream. To evaluate the effectiveness of these treatments, this paper presents a data-driven method to conduct this research by collecting data during five seasons of heavy rainfalls. Survival analysis, Bootstrap method and Extreme Learning Machine(ELM) are selected to build data-driven models. By implementing survival analysis models, the survival probabilities of locations without treatment decrease to 0%.The survival probability of the locations with treatment stay at 55.6%after the five rainfall seasons. Meanwhile, the maximum hourly rainfall in the post-treatment period is 2.571 times as the one in pre-treatment period. Total rainfall volume of post-treatment period is 1.232 times as the one in pre-treatment period. Total time period of rainfall of post-treatment is 5.435 times as the one of pre-treatment. Comparing the survival probabilities, the effect of treatments are significant in the prevention of debris flows. After resampling by bootstrap method, the predictive results from Extreme Learning Machine indicate that, without geological treatment, the probability of having debris flow under 10 times of heavy rainfall is 100%.It is much higher than the observed 30% between the year of 2011 and 2015. The predicted debris flows magnitudes are also significantly higher than the observed ones with treatment. Hence, geological engineering treatment is crucial in reducing and preventing geohazards.

       

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