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高速铁路车站岔区高填方路基沉降组合预测研究 被引量:4

Study on Settlement Combination Prediction of High Fill Subgrade in Turnout Area of High-speed Railway Station
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摘要 为确定高速铁路车站岔区高填方路基工后沉降是否满足铺设无砟轨道要求,结合现场监测沉降数据,采用三种单项模型(V模型、D模型和H模型)对沉降进行预测;利用最优组合原理建立邓英尔-双曲线组合模型(D-H模型)、邓英尔-灰色费尔哈斯组合模型(D-V模型)、双曲线-灰色费尔哈斯组合模型(H-V模型)3种两模型组合模型和1种三模型组合模型(D-H-V模型)。进一步探讨各预测模型的适用性和可靠性,引入5个精度评价指标,对各模型预测效果进行评价,预测效果优劣顺序为:三模型组合模型>两模型组合模型>邓英尔模型>灰色Verhulst模型>双曲线模型。用各断面的最优模型预测工后沉降,各断面工后沉降均满足铺设无砟轨道要求。 In order to determine whether the post construction settlement of high fill subgrade in the turnout area of high-speed railway station meets the requirements of laying ballastless track,combined with the field monitoring settlement deformation data,firstly,three single models(V model,D model and H model)were used to predict the settlement.Then,four new prediction models were established by using the optimal combination principle,including three types of two-model combination models,namely,the Dengyinger-hyperbolic combination model(D-H model),the Dengyinger-grey Verhulst combination model(D-V model),and the hyperbolic-grey Verhulst combination model(H-V model),and one three-model combination model(D-H-V model).In order to further explore the applicability and reliability of each prediction model,five accuracy evaluation indexes were introduced to evaluate the prediction effect of each model.The prediction effect is in the order of three-model combination model,two-model combination model,Dengyinger model,grey Verhulst model and hyperbolic model.Finally,the optimal model of each section was used to predict the post construction settlement,and it is found that the post construction settlement of each section meets the requirements of laying ballastless track.
作者 马学宁 陈玉燕 王旭 MA Xuening;CHEN Yuyan;WANG Xu(School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处 《铁道学报》 EI CAS CSCD 北大核心 2023年第1期105-113,共9页 Journal of the China Railway Society
基金 国家自然科学基金(41562014)。
关键词 高速铁路 高填方路基 沉降预测 单项预测模型 组合预测模型 high-speed railway high fill subgrade settlement prediction single prediction model combination prediction model
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