基于动态GM-Markov优化模型的高铁路基冻胀变形预测研究 |
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引用本文: | 梁斌,魏冠军,张幸,张沛. 基于动态GM-Markov优化模型的高铁路基冻胀变形预测研究[J]. 路基工程, 2022, 0(4): 7-12. DOI: 10.13379/j.issn.1003-8825.202107049 |
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作者姓名: | 梁斌 魏冠军 张幸 张沛 |
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作者单位: | 1.兰州交通大学测绘与地理信息学院,兰州 730070 |
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基金项目: | 国家自然科学基金项目:基于数据同化的高铁路基冻胀变形分析与时空预报研究(41964008);兰州交通大学“百名青年优秀人才培养计划”(152022);受兰州交通大学优秀平台支持(201806) |
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摘 要: | 由于传统灰色模型在预测波动性较大的数据时精度不高,提出一种改进的动态GM-Markov预测模型。利用非等间距加权矩阵与无偏优化对灰色模型进行改进,通过原始序列的动态更新实现模型的参数更新,在此基础上利用马尔科夫模型进行残差修正,得到改进动态GM-Markov预测模型。利用某高铁路基冻胀变形监测数据进行实例分析,将改进的动态GM-Markov模型预测结果与灰色以及非等间距无偏灰色模型、最优组合模型预测结果进行对比分析,结果表明:改进的动态GM-Markov模型对于波动性较大的冻胀变形可以取得较好预测效果,提高了预测精度与稳定性。
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关 键 词: | 高铁路基 动态预测 冻胀变形 马尔科夫模型 灰色模型预测 |
收稿时间: | 2021-09-30 |
Prediction of Frost Heave Deformation of High-speed Railway Subgrade Based on Dynamic GM-Markov Optimization Model |
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Abstract: | Because the accuracy of the traditional grey model is not high in predicting the data with high fluctuation, an improved dynamic GM-Markov prediction model has been proposed. The grey model has been improved by non-equal spacing weighted matrix and unbiased optimization, and the parameters of the model are updated by dynamic updating of original sequence. On this basis, the improved dynamic GM-Markov prediction model is obtained by residual correction of Markov model. Based on the monitoring data of the frost heaving deformation of a high-speed railway foundation, the improved dynamic GM-Markov model prediction results has been compared with the prediction results of grey model, non-equidistance unbiased grey model and optimal combined model. The results show that the improved dynamic GM-Markov model can achieve better prediction effect for frost heave deformation with large fluctuation and improve the prediction accuracy and stability. |
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