隧道围岩收敛变形改进GM(1,1)预测模型研究
首发时间:2014-07-01
摘要:为了有效地提高隧道围岩变形预测精度,对传统GM(1,1)预测模型进行了改进。改进模型通过对原始监测数据列优化重构,降低了量测误差、外界因素等噪声干扰造成的监测数据随机突变和离散性,提高模型预测效果,并结合梅花山隧道典型监测断面对改进GM(1,1)预测模型进行了检验。研究结果表明:改进GM(1,1)预测模型增强了环境适应能力,预测结果与实际监测数据吻合程度明显地提高,具有较好的实际工程应用推广价值。
关键词: 隧道围岩 改进GM(1,1)模型 收敛变形 重构参数
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Tunnel surrounding rock deformation based on improved GM (1, 1) prediction model
Abstract:The traditional gray system GM (1,1) prediction model is improved in order to effectively improve the prediction accuracy of tunnel surrounding rock deformation. By reconstructing original monitoring data, the new prediction model reduces the random mutation and discreteness in monitoring data, which are caused by the noise interference such as measurement error and external factors, and improves the prediction reliability. Taking a typical monitoring section of Meihuashan tunnel for example, the improved GM (1,1) prediction model was tested. The results showed that improved GM (1,1) prediction model has better prediction results which were in good accordance with the monitoring data in situ and could be applied in actual engineering.
Keywords: surrounding rock improved GM (1,1) model deformation prediction reconstruction parameter
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