华东地区夏季无砟轨道温度梯度预警研究
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摘要
为研究华东地区夏季无砟轨道温度梯度的分布规律同时对高温时期的温度梯度进行预警管理,运用统计学方法研究轨道板温度梯度的分布规律并得到其预警限值,同时构建贝叶斯网络预测模型,对华东地区夏季无砟轨道温度梯度质量进行预测与评价。研究结果表明:华东地区夏季正温度梯度预警限值为66. 5℃/m,负温度梯度预警限值为-31. 5℃/m;贝叶斯网络预测模型具有88. 6%的准确率,可良好预测无砟轨道温度梯度的质量等级,同时由贝叶斯的诊断推理功能得出环境温度和太阳辐射是造成无砟轨道温度梯度异常的主要原因。
In order to study the distribution of temperature gradient in the ballastless track in summer in East China and manage the temperature gradient in high temperature period,statistical methods are employed to study the distribution law of the temperature gradient of the track plate and obtain the warning limit. Meanwhile,Bayesian network prediction model is constructed to predict and evaluate the temperature gradient quality of the ballastless track. The results of the study indicate that the pre-warning limit for positive temperature gradients in summer in East China is 66. 5 ℃/m,and that for negative temperature gradients is-31. 5 ℃/m; the Bayesian network prediction model enjoys 88. 6% accuracy to ensure excellent prediction of the quality grade of the track temperature gradient. The Bayesian diagnostic inference function indicates that ambient temperature and solar radiation are the main causes of temperature gradient in ballastless track.
In order to study the distribution of temperature gradient in the ballastless track in summer in East China and manage the temperature gradient in high temperature period,statistical methods are employed to study the distribution law of the temperature gradient of the track plate and obtain the warning limit. Meanwhile,Bayesian network prediction model is constructed to predict and evaluate the temperature gradient quality of the ballastless track. The results of the study indicate that the pre-warning limit for positive temperature gradients in summer in East China is 66. 5 ℃/m,and that for negative temperature gradients is-31. 5 ℃/m; the Bayesian network prediction model enjoys 88. 6% accuracy to ensure excellent prediction of the quality grade of the track temperature gradient. The Bayesian diagnostic inference function indicates that ambient temperature and solar radiation are the main causes of temperature gradient in ballastless track.
引文
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[2]国家铁路局. TB10621—2014高速铁路设计规范[S].北京:中国铁道出版社,2015.
[3]赵坪锐,刘学毅,杨荣山,等.双块式无砟轨道温度荷载取值方法的试验研究[J].铁道学报,2016,38(1):92-97.
[4]刘学毅.客运专线无砟轨道设计理论与方法[M].成都:西南交通大学出版社,2010:106-115.
[5]杨荣山,万章博,刘学毅,等. CRTSⅠ型双块式无砟轨道冬季温度场试验[J].西南交通大学学报,2015,50(3):454-460.
[6]戴公连,温学桧,苏海霆.寒冷季节桥上无砟轨道横竖向温度梯度研究[J].华中科技大学学报(自然科学版),2015,43(7):1-5.
[7]欧祖敏,孙璐,程群群.基于气象资料的无砟轨道温度场计算与分析[J].铁道学报,2014,36(11):106-122.
[8]闫斌,戴公连,苏海霆.基于气象参数的轨道板竖向温度梯度预测模型[J].华南理工大学学报(自然科学版),2014,42(12):9-13.
[9] Zhao C Y. Review on thermal transport in high porosity cellular metal foams with open cells[J]. International Journal of Heat&Mass Transfer,2013,44(36):3618-3632.
[10] Caratelli A,Meda A,Rinaldi Z,et al. Structural behaviour of precast tunnel segments in fiber reinforced concrete[J]. Tunnelling&Underground Space Technology Incorporating Trenchless Technology Research,2011,26(2):284-291.
[11]李艳美,张卓奎.基于贝叶斯网络的数据挖掘方法[J].计算机仿真,2008,25(2):87-89.
[12] Khakzad N,Khan F,Amyotte P. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network[J]. Process Safety&Environmental Protection,2013,91(1-2):46-53.
[13] Pelikan M,Goldberg D E,Cantú-Paz E. Linkage problem,distribution estimation,and Bayesian networks[J]. Evolutionary Computation,2014,8(3):311-340.
[14] Chang K C,Fung R,Lucas A,R Oliver,N Shikaloff. Bayesian networks applied to credit scoring[J]. Ima Journal of Management Mathematics,2018,11(1):1-18.
[15] Cook J,Lewandowsky S. Rational Irrationality:Modeling Climate Change Belief Polarization Using Bayesian Networks[J]. Topics in Cognitive Science,2016,8(1):160-179.