基于PLS方法的咸阳地区黄土导热系数预测模型研究
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摘要
岩土热物性参数是热工计算的基础参数。为探讨咸阳地区不同沉积年代黄土的导热系数随物理参数的变化规律,选取区内95组黄土的含水率、干密度、孔隙比及导热系数室内试验数据,采用偏最小二乘回归原理(PLS),辨识与解释了参数变量间的相关关系,建立了咸阳黄土塬区不同沉积年代黄土的导热系数预测模型。结果表明:黄土的导热系数随干密度的增大而增大,随孔隙比的减小而增大;随黄土沉积年代越久远,导热系数呈非线性增长,中更新世晚期至早期的增幅最大,增幅为8. 70%。
Geothermal parameters are the basic parameters for thermal calculation. In order to discuss the variation law of thermal conductivity,we know of the physical parameters of loess in different geological periods in Xianyang area. The experimental data of 95 groups of loess were selected,and partial least-squares method was used to analyze the relationship between the parameters. The prediction model of loess thermal conductivity in different geological periods was established. The results show that the thermal conductivity of loess increases with the increase of dry density,and it also increases with the decrease of void ratio. The longer the loess deposition age is,the higher the thermal conductivity increases. The increase from the late to the early period of the Middle Pleistocene was the largest with an increase of 8. 70%.
Geothermal parameters are the basic parameters for thermal calculation. In order to discuss the variation law of thermal conductivity,we know of the physical parameters of loess in different geological periods in Xianyang area. The experimental data of 95 groups of loess were selected,and partial least-squares method was used to analyze the relationship between the parameters. The prediction model of loess thermal conductivity in different geological periods was established. The results show that the thermal conductivity of loess increases with the increase of dry density,and it also increases with the decrease of void ratio. The longer the loess deposition age is,the higher the thermal conductivity increases. The increase from the late to the early period of the Middle Pleistocene was the largest with an increase of 8. 70%.
引文
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[2]阮传侠,冯树友,牟双喜,等.天津地区地层热物性特征及影响因素分析[J].水文地质工程地质,2017,44(5):158-162.
[3]官燕玲,张小刚,梁草茹,等.西安地区土壤源热泵地埋管换热的岩土影响因素区域分布[J].西北大学学报,2016,46(4):566-572.
[4]陈斌,叶俊能,朱剑锋,等.宁波轨道交通地基土体物理参数概率分布及相关性研究[J].工程力学,2013(30):200-205.
[5]张贵金,徐卫亚.岩土工程参数多重相关性的度量[J].岩石力学与工程学报,2004,23(7):1109-1113.
[6]彭必建,李四堂,康鹏,等.昆明盆地浅层岩土体热物性规律的试验分析[J].中国西部科技,2014,13(2):4-8.
[7]孙婉,高世轩,黄坚,等.上海地区各土层热物性及分布特征研究[J].人民长江,2015,45(2):97-99.
[8]叶万军,董西好,杨更社,等.含水率和干密度对黄土热参数影响的试验研究[J].岩土力学,2017,38(3):656-661.
[9]安可栋,王文科,王周峰,等.风积沙热参数计算及其与含水率关系研究[J].水文地质工程地质,2015,42(5):129-133.
[10]刘爱华,佟红兵,冉伟彦.北京某垂直地埋管区地温场变化规律研究[J].水文地质工程地质,2016,43(4):165-170.
[11]王铁行,刘自成,卢靖.黄土导热系数和比热容的试验研究[J].岩土力学,2007,28(4):655-658.
[12]孙彩云,常梦颖.对北京市年需水量预测模型的研究[J].数理统计与管理,2017,36(6):1050-1058.
[13]孙彩云,李洪伟.基于均匀设计试验利用偏最小二乘回归对边坡稳定性的分析预测[J].数学的实践与认识,2018,48(4):198-204.
[14] Lima S L T,Mello C,Poppi R J. PLS pruning:a new approach to variable selection for multivariate calibration based on Hessian matrix of errors[J]. Chemometrics and Intelligent Laboratory Systems,2005,76(1):73-78.
[15] Gauchi J P,Chagnon P. Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data[J]. Chemometrics and Intelligent Laboratory Systems,2001,58(2):171-193.