Research on experimental tests and prediction models of thermal conductivity of freezing-thawing soil in the Kunlun Mountains

LIU Zhiyun; ZHANG Wei; WANG Wei; CUI Fuqing

doi:10.16030/j.cnki.issn.1000-3665.202003003

2021 Vol. 48, No. 1

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LIU Zhiyun, ZHANG Wei, WANG Wei, CUI Fuqing. Research on experimental tests and prediction models of thermal conductivity of freezing-thawing soil in the Kunlun Mountains[J]. Hydrogeology & Engineering Geology, 2021, 48(1): 105-113. doi: 10.16030/j.cnki.issn.1000-3665.202003003

Citation:

LIU Zhiyun, ZHANG Wei, WANG Wei, CUI Fuqing. Research on experimental tests and prediction models of thermal conductivity of freezing-thawing soil in the Kunlun Mountains[J]. Hydrogeology & Engineering Geology, 2021, 48(1): 105-113. doi: 10.16030/j.cnki.issn.1000-3665.202003003

Research on experimental tests and prediction models of thermal conductivity of freezing-thawing soil in the Kunlun Mountains

1.
College of Geological Engineering and Geomatics, Chang’an University, Xi’an, Shaanxi　710054, China
2.
State Key Laboratory of Road Engineering Safety and Health in Cold and High-Altitude Regions, CCCC First Highway Consultants Co. Ltd., Xi’an, Shaanxi　710065, China

More Information

Corresponding author: CUI Fuqing, cfq731@chd.edu.cn

Received Date: 02 March 2020

Revised Date: 25 May 2020

Publish Date: 15 January 2021

Abstract

Abstract

In order to explore the basic laws of freezing and thawing soil in the Qinghai-Tibet Engineering Corridor in the Kunlun Mountains area, the coefficient of thermal conductivity of 349 groups of drilling frozen soil samples and 245 groups of thawing soil samples is tested by the transient plane heat source method. The characteristics of five kinds of soil thermal conductivity distribution and natural moisture content, dry density and the partial correlation coefficient of thermal conductivity are analyzed, and the experience for both variables in fitting formula, support vector regression (SVR) and radial basis (RBF) neural network prediction model of thermal conductivity are established. The results show that the thermal conductivity of freezing-thawing soil is larger than that of fine-grained soil, and the thermal conductivity of freezing-thawing soil varies with the distribution of soil properties. Natural moisture content and dry density are positively correlated with thermal conductivity, and the partial correlation results of different soil types are significantly different. The binary empirical regression equation of typical soil thermal conductivity is shown as a nonlinear fitting result. The results of thermal conductivity prediction of the typical soil and freezing-thawing soil under three prediction models show that the prediction effect of fully weathered phyllite, breccia and gravel sand is better, and the prediction accuracy of SVR and RBF neural network of silty soil is also better. On the whole, the prediction effect of thermal conductivity on thawed soil is slightly better than that of frozen soil, and the prediction accuracy of thermal conductivity of breccias, silty soil and fully weathered meltwater is higher under the SVR and RBF neural network models. The prediction results and error analysis of the three thermal conductivity models show that the prediction results of the SVR and RBF neural network models are significantly better than that of the empirical fitting equation method. The prediction effect of SVR and RBF neural network prediction models varies with different soil thermal conductivities, and the overall prediction effect is similar, with higher prediction accuracy and wider range of soil application.
- Kunlun Mountains area /
- thermal conductivity /
- partial correlation analysis /
- SVR /
- RBF neural network /
- frozen soil

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References

[1]	JIN Huijun, ZHAO Lin, WANG Shaoling, et al. Thermal regimes and degradation modes of permafrost along the Qinghai-Tibet Highway[J]. Science in China(Series D: Earth Sciences),2006 49(11):1170 − 1183. Google Scholar
[2]	PENG Hui, MA Wei, MU Yanhu, et al. Degradation characteristics of permafrost under the effect of climate warming and engineering disturbance along the Qinghai–Tibet Highway[J]. Natural Hazards,2015,75(3):2589 − 2605. doi: 10.1007/s11069-014-1444-5 CrossRef Google Scholar
[3]	董元宏, 彭惠, 罗滔, 等. 气候变暖背景下拟建青藏高速公路沿线典型区段多年冻土未来50年退化特征[J]. 灾害学,2019,34(增刊1):20 − 25. [DONG Yuanhong, PENG Hui, LUO Tao, et al. Degradation characteristics of the permafrost at typical sites along Qinghai-Tibet expressway in the next 50 years under climate warming background[J]. Journal of Catastrophology,2019,34(Sup1):20 − 25. (in Chinese with English abstract) Google Scholar
[4]	徐敩祖, 陶兆祥, 傅素兰. 典型融冻土的热学性质[C]//中国科学院兰州冰川冻土研究所集刊第2号. 北京: 科学出版社, 1981. Google Scholar XU Xiaozu, TAO Zhaoxiang, FU Sulan. Thermal properties of typical unfrozen and frozen soil[C]//Memoirs of Lanzhou Institute of Glaciology and Crypedology, Academia Sinica(Vol.2). Beijing: Science Press, 1981. (in Chinese) Google Scholar
[5]	肖忠华. 上海软土二次冻融土工程性质试验研究[D]. 上海: 同济大学, 2007. Google Scholar XIAO Zhonghua. Experimental investigation on engineering properties of Shanghai soft soils under secondary freeze-thaw action[D]. Shanghai: Tongji University, 2007. (in Chinese with English abstract) Google Scholar
[6]	尹飞. 冻土导热系数的仪器研制和稳态法模拟试验研究[D]. 长春: 吉林大学, 2008. Google Scholar YIN Fei. The instrumental development of thermal conductivity of frozen ground and the research on the steady-state method simulation test[D]. Changchun: Jilin University, 2008. (in Chinese with English abstract) Google Scholar
[7]	王伟. 冻土传热性质试验研究[D].长春: 吉林大学, 2010. Google Scholar WANG Wei. The experiments research on thermal conductivity of frozen soil[D]. Changchun: Jilin University, 2010.(in Chinese with English abstract) Google Scholar
[8]	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 − 163. [RUAN Chuanxia, FENG Shuyou, MOU Shuangxi, et al. An analysis of the characteristics of thermal physical properties and their influencing factors in the Tianjin area[J]. Hydrogeology & Engineering Geology,2017,44(5):158 − 163. (in Chinese with English abstract) Google Scholar
[9]	李思齐, 杨平, 赵方舟. 砾石地层冻土热物理特性研究[J]. 水文地质工程地质,2008,45(6):122 − 126. [LI Siqi, YANG Ping, ZHAO Fangzhou. A study of the thermal physical properties of frozen soil in gravel layers[J]. Hydrogeology & Engineering Geology,2008,45(6):122 − 126. (in Chinese with English abstract) Google Scholar
[10]	姜雄. 多年冻土区高温冻土导热系数试验研究[D]. 徐州: 中国矿业大学, 2015. Google Scholar JIANG Xiong. Experimental study on thermal conductivity for warm frozen soils in permafrost regions[D].Xuzhou: China University of Mining and Technology, 2015.(in Chinese with English abstract) Google Scholar
[11]	张婷, 杨平. 不同因素对浅表土导热系数影响的试验研究[J]. 地下空间与工程学报,2012,8(6):1233 − 1238. [ZHANG Ting, YANG Ping. Effect of different factors on the heat conduction coefficient of shallow top soil[J]. Chinese Journal of Underground Space and Engineering,2012,8(6):1233 − 1238. (in Chinese with English abstract) Google Scholar
[12]	何玉洁, 宜树华, 郭新磊. 青藏高原含砂砾石土壤导热率实验研究[J]. 冰川冻土,2017,39(2):343 − 350. [HE Yujie, YI Shuhua, GUO Xinlei. Experimental study on thermal conductivity of soil with gravel on the Qinghai-Tibet Plateau[J]. Journal of Glaciology and Geocryology,2017,39(2):343 − 350. (in Chinese with English abstract) Google Scholar
[13]	温智, 盛煜, 马巍, 等. 青藏高原北麓河地区原状多年冻土导热系数的试验研究[J]. 冰川冻土,2005 27(2):182 − 187. [WEN Zhi, SHENG Yu, MA Wei, et al. Experimental studies of thermal conductivity of undisturbed permafrost at Beiluhe testing site on the Tibetan plateau[J]. Journal of Glaciology and Geocryology,2005 27(2):182 − 187. (in Chinese with English abstract) doi: 10.3969/j.issn.1000-0240.2005.02.005 CrossRef Google Scholar
[14]	JOHANSEN O. Thermal conductivity of soils[D]. Norway: Trondheim University, 1975. Google Scholar
[15]	CÔTÉ J, KONRAD J M. A generalized thermal conductivity model for soils and construction materials[J]. Canadian Geotechnical Journal,2005,42(2):443 − 458. doi: 10.1139/t04-106 CrossRef Google Scholar
[16]	ZHU Ming. Modeling and simulation of frost heave in frost-susceptible soils[D]. Ann Arbor: University of Michigan, 2006. Google Scholar
[17]	罗斯琼, 吕世华, 张宇, 等. 青藏高原中部土壤热传导率参数化方案的确立及在数值模式中的应用[J]. 地球物理学报,2009,52(4). [LUO Siqiong, LV Shihua, ZHANG Yu, et al. Soil thermal conductivity parameterization establishment and application in numerical model of central Tibetan Plateau[J]. Chinese Journal of Geophysics,2009,52(4). (in Chinese with English abstract) doi: 10.3969/j.issn.0001-5733.2009.04.030 CrossRef Google Scholar
[18]	李顺群, 陈之祥, 夏锦红, 等. 冻土导热系数的聚合模型研究及试验验证[J]. 中国公路学报,2018,31(8):39 − 46. [LI Shunqun, CHEN Zhixiang, XIA Jinhong, et al. Aggregation model research and experimental verification of frozen soil thermal conductivity[J]. China Journal of Highway and Transport,2018,31(8):39 − 46. (in Chinese with English abstract) Google Scholar
[19]	戚家忠, 储党生, 韩彦智. 祁东矿人工冻土物理力学性能试验研究[J]. 淮南工业学院学报,1999 19(3):3 − 5. [QI Jiazhong, CHU Dangsheng, HAN Yanzhi. Experimental study on physical and mechanical properties of artificial frozen soil in Qidong Mine[J]. Journal of Huainan Institute of Technology(Natural Science),1999 19(3):3 − 5. (in Chinese with English abstract) Google Scholar
[20]	洪涛, 梁四海, 孙禹, 等. 黄河源区多年冻土热传导系数影响因素分析及其在活动层厚度模拟中的应用[J]. 冰川冻土,2013,35(4):824 − 833. [HON G Tao, LIANG Sihai, SUN Yu, et al. Analyzing the factors that impact on the heat conductivity coefficient and applying them to simulate the depth of permafrost active layer in the headwaters of the Yellow River[J]. Journal of Glaciology and Geocryology,2013,35(4):824 − 833. (in Chinese with English abstract) Google Scholar
[21]	何发祥, 黄英. 用BP网络求解土体的导热系数[J]. 岩土力学,2000, 21(1):84 − 87. [HE Faxiang, HUANG Ying. Solution of thermal conduction coefficient from BP network[J]. Rock and Soil Mechanics,2000, 21(1):84 − 87. (in Chinese with English abstract) doi: 10.3969/j.issn.1000-7598.2000.01.021 CrossRef Google Scholar
[22]	袁玉倩, 薛桂香, 孙春华, 等. 基于BP神经网络的土壤热导率预测算法研究[J]. 河北工业大学学报,2015,44(6):39 − 44. [YUAN Yuqian, XUE Guixiang, SUN Chunhua, et al. A study of soil thermal conductivity prediction algorithm based on BP neural network[J]. Journal of Hebei University of Technology,2015,44(6):39 − 44. (in Chinese with English abstract) Google Scholar
[23]	BANG H T, YOON S, JEON H. Application of machine learning methods to predict a thermal conductivity model for compacted bentonite[J]. Annals of Nuclear Energy,2020,142:107395. doi: 10.1016/j.anucene.2020.107395 CrossRef Google Scholar
[24]	周幼吾, 郭东信, 邱国庆.中国冻土[M]. 北京: 科学出版社, 2010. Google Scholar ZHOU Youwu, GUO Dongxin, QIU Guoqing. Geocryology in China[M]. Beijing: Science Press, 2010. (in Chinese) Google Scholar
[25]	LUTS J, OJEDA F, PLAS R V D, et al. A tutorial on support vector machine-based methods for classification problems in chemometrics[J]. Analytica Chimica Acta,2010,665(2):129 − 145. doi: 10.1016/j.aca.2010.03.030 CrossRef Google Scholar
[26]	LIU Y, PARHI K K. Computing RBF Kernel for SVM Classification Using Stochastic Logic[C]//IEEE International Workshop on Signal Processing Systems. IEEE, 2016: 327−332. Google Scholar
[27]	周维华. RBF神经网络隐层结构与参数优化研究[D].上海: 华东理工大学, 2014. Google Scholar ZHOU Weihua. Optimization study of the hidden structure and parameter in the RBF neural networks[D]. Shanghai: East China University of Science and Technology, 2014. (in Chinese with English abstract) Google Scholar