郑西高铁K1045+800路基工后沉降监测与预测
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摘要
路基工后沉降是影响高速铁路安全运行的重要因素之一,开展高速铁路路基工后沉降监测与预测研究对提高高铁运行安全具有一定的工程意义。以郑西高铁K1045+800路基为例,通过设计合理的监测方案监测了路基不同部位的工后沉降、沉降速率和湿度,分析了路基工后沉降规律;基于监测数据采用GM(1,1)模型、神经网络模型和灰色神经网络模型进行了路基工后沉降预测,在验证预测效果的基础上基于灰色神经网络模型构建了路基工后沉降预测模型,明确了该路基的稳定状况并提出了路基工后沉降预警方法。研究结果表明:Ⅰ-1、Ⅰ-2、Ⅰ-3和Ⅰ-4监测点的工后沉降预测值在第45监测期以后增量很小,并分别收敛于5.19mm、4.96mm、4.71mm和4.47mm,该路基将继续保持稳定状态;选取工后沉降量为主要预警指标,沉降速率为辅助预警指标,将高铁路基沉降预警等级分为Ⅰ、Ⅱ和Ⅲ级。
Subgrade post-construction settlement is one of the most important factors affecting the safe operation of high-speed railway,research on subgrade post-construction settlement monitoring and prediction has a certain engineering significance to improve high-speed railway safe operation. With Zhengzhou-Xi'an high-speed railway K1045 +800 subgrade as an example, through the reasonable design of monitoring scheme, the post-construction settlement, settlement velocity and humidity monitoring were managed, and the law of post-construction settlement was also summarized; Post-construction settlement was predicted using the GM(1, 1) model, the neural network model and the grey neural network model based on the monitoring data; Subgrade post-construction settlement prediction model based on the gray neural network model was constructed on the basis of prediction accuracy authentication, and the warning method of subgrade post-construction settlement was established. The research results show that the post-construction settlement of Ⅰ-1, Ⅰ-2, Ⅰ-3 and Ⅰ-4 monitoring sites become very little after 45 monitoring cycle, and the post-construction settlement will convergent to 5.19 mm, 4.96 mm, 4.71 mm and 4.47 mm,the subgrade will continue to keep the stable state; Post-construction settlement and settlement velocity are chosen as main warning index and auxiliary warning index, high-speed railway subgrade post-construction settlement warning grades are divided into Ⅰ,Ⅱ and Ⅲ.
Subgrade post-construction settlement is one of the most important factors affecting the safe operation of high-speed railway,research on subgrade post-construction settlement monitoring and prediction has a certain engineering significance to improve high-speed railway safe operation. With Zhengzhou-Xi'an high-speed railway K1045 +800 subgrade as an example, through the reasonable design of monitoring scheme, the post-construction settlement, settlement velocity and humidity monitoring were managed, and the law of post-construction settlement was also summarized; Post-construction settlement was predicted using the GM(1, 1) model, the neural network model and the grey neural network model based on the monitoring data; Subgrade post-construction settlement prediction model based on the gray neural network model was constructed on the basis of prediction accuracy authentication, and the warning method of subgrade post-construction settlement was established. The research results show that the post-construction settlement of Ⅰ-1, Ⅰ-2, Ⅰ-3 and Ⅰ-4 monitoring sites become very little after 45 monitoring cycle, and the post-construction settlement will convergent to 5.19 mm, 4.96 mm, 4.71 mm and 4.47 mm,the subgrade will continue to keep the stable state; Post-construction settlement and settlement velocity are chosen as main warning index and auxiliary warning index, high-speed railway subgrade post-construction settlement warning grades are divided into Ⅰ,Ⅱ and Ⅲ.
引文
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[9]朱才辉,李宁.基于修正FDA方法的黄土高填方地基工后沉降分析[J].岩石力学与工程学报,2015,34(增10):3408-3417.
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[11]高红,文鸿雁,李运健,等.基于小波变换与卡尔曼滤波结合的GM(1,1)模型在高铁隧道沉降变形分析中的应用[J].现代隧道技术,2016,53(4):84-89.
[12]章红兵,范凡,胡昊.考虑基坑开挖空间效应的邻近建筑物沉降预测方法[J].上海交通大学学报,2016,50(4):641-646.
[13]李欣.郑西高铁湿陷黄土路基沉降监测及预警系统研究[D].西安:长安大学,2012.
[14]Xiao Xinping,Hu Yichen,Guo Huan.Modeling mechanism and extension of GM(1,1)[J].Journal of Systems Engineering and Electronics,2013,24(3):445-453.
[15]Leea K Y,Chungb N,Suntae H.Application of an artificial neural network(ANN)model for predicting mosquito abundances in urban areas[J].Ecological Informatics,2016(36):172-180.
[2]Heureux J S,Long M,Vanneste M,et al.On the prediction of settlement from high-resolution shear-wave reflection seismic data:The Trondheim harbor case study,mid Norway[J].Engineering Geology,2013(167):72-83.
[3]Andrew H T,Bruce W.Large mammal biomass predicts the changing distribution of hunter-gatherer settlements in mid-late Holocene Alaska[J].Journal of Anthropological Archaeology,2017(45):81-97.
[4]颜早璜,刘玉山,张宇.高速公路工后沉降监测方法及预测研究[J].低温建筑技术,2013(9):117-119.
[5]Yang J,Feng Q B.A new method for measuring subgrade settlement in high-speed railway by using a linear CCD[J].Measurement,2013(46):1751-1756.
[6]郭健,查吕应,庞有超,等.基于小波分析的深基坑地表沉降预测研究[J].岩土工程学报,2014,36(增2):343-347.
[7]肖衡林,李丽华,郭小川,等.山区高填方路堤沉降预测方法[J].应用基础与工程科学学报,2015,23(1):154-161.
[8]谭衢霖,魏健,胡吉平.基于小波神经网络的建筑工程沉降变形预测[J].应用基础与工程科学学报,2015,23(3):629-636.
[9]朱才辉,李宁.基于修正FDA方法的黄土高填方地基工后沉降分析[J].岩石力学与工程学报,2015,34(增10):3408-3417.
[10]Chen R P,Cheng W,Jia R Y,et al.Numerical study of High Speed Railway subgrade settlement induced by Underwater Lowering[J].Procedia Engineering,2016(143):1350-1357.
[11]高红,文鸿雁,李运健,等.基于小波变换与卡尔曼滤波结合的GM(1,1)模型在高铁隧道沉降变形分析中的应用[J].现代隧道技术,2016,53(4):84-89.
[12]章红兵,范凡,胡昊.考虑基坑开挖空间效应的邻近建筑物沉降预测方法[J].上海交通大学学报,2016,50(4):641-646.
[13]李欣.郑西高铁湿陷黄土路基沉降监测及预警系统研究[D].西安:长安大学,2012.
[14]Xiao Xinping,Hu Yichen,Guo Huan.Modeling mechanism and extension of GM(1,1)[J].Journal of Systems Engineering and Electronics,2013,24(3):445-453.
[15]Leea K Y,Chungb N,Suntae H.Application of an artificial neural network(ANN)model for predicting mosquito abundances in urban areas[J].Ecological Informatics,2016(36):172-180.