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Study on Dynamic Stability Prediction Model of Slope in Eastern Tibet Section of Sichuan-Tibet Highway

机译:川藏公路藏东段边坡动态稳定性预测模型研究

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摘要

The stability of the slope along the middle section of Tibet controls the safety and smoothness of the Sichuan-Tibet highway, which is affected by multiple and uncertain factors such as rainfall. The slope dynamic stability is evaluated to the benefit of that salvager can prepare in advance and preserve timely and accurately. Therefore, engineering treatment scheme in different batches, stages, and grades can be proposed prospectively. Random Forest algorithm was used to rank 10 primary factors: precipitation, earthquake, human factors, groundwater, slope height, slope gradient, dense degree, weathering depth, vegetation, and slope shape. Considering precipitation and earthquake as dynamic factors, a wavelet and NARX dynamic neural network were used to predict the trend and quantity of precipitation and earthquake, followed by developing a dynamic stability evaluation model by combining a fuzzy neural network model with other indexes. Results show that (1) the superposition error in rainfall and earthquake prediction is 0.21, proving that the ranking of influencing factors is reasonable, and (2) the back-judgment and test accuracy of the dynamic evaluation model are 93.98 and 91.67, respectively, indicating that the model is accurate and applicable. The model can evaluate the dynamic stability of slopes and provide more reasonable engineering protection countermeasures so that Highway Public Works Department can deal with emergencies and disasters timely and precisely.
机译:沿西藏中段边坡的稳定性控制着川藏公路的安全畅通性,受降雨等多重不确定因素影响。对边坡动态稳定性进行评估,以便打捞者能够提前做好准备并及时准确地保存。因此,可以前瞻性地提出不同批次、不同阶段、不同等级的工程处理方案。采用随机森林算法对降水量、地震、人为因素、地下水、边坡高度、边坡坡度、密度度、风化深度、植被、边坡形状等10个主要因子进行排序。以降水和地震为动力因子,采用小波和NARX动态神经网络预测降水和地震的趋势和数量,然后结合模糊神经网络模型与其他指标建立动态稳定性评价模型。结果表明:(1)降雨与地震预报的叠加误差为0.21%,证明影响因素排序是合理的;(2)动态评价模型的回判和检验准确率分别为93.98%和91.67%,表明该模型具有准确性和适用性。该模型可以评估边坡的动态稳定性,并提供更合理的工程保护对策,使公路工务部门能够及时、准确地处理突发事件和灾害。

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    Department of Construction Engineering Dalian University of Technology Dalian 116024 ||Sichuan College of Architectural Technology Deyang 618000;

    College of Marine Geosciences Ocean University of China Qingdao 266100;

    Department of Construction Engineering Dalian University of Technology Dalian 116024Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu 610031;

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