基于多元分布模型预测土体热阻系数

doi:10.13722/j.cnki.jrme.2022.0643

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摘要土体热阻系数是决定岩土材料热性质的重要参数。为准确预测土体热阻系数，基于多元分布模型建立土体热阻系数预测模型，对所建立的预测模型进行验证分析，并与传统经验关系模型进行对比，明确所建多元分布模型的准确性。结果表明：建立的多元分布模型能够准确预测土体热阻系数，随着输入参数增加多元分布模型预测精度显著提升，其相关系数从0.719 5提高到0.899 5，平均值值从1.077 8降低到1.037，变异系数值从0.367 5降低到0.212 1，预测精度最好的是模型，多元分布模型预测精度明显优于传统经验关系模型；对于工程性质差异显著、沉积环境复杂的不同类型土体热阻系数预测，建议根据资料选择不同类型的多元分布模型来评价其热阻系数。

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	王才进1
	武猛1
	蔡国军1
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	赵泽宁1
	刘松玉1

关键词 ：土力学, 热阻系数, 正态分布, 多元分布模型, 预测模型

Abstract：The soil thermal resistivity is an important parameter to determine the thermal properties of geotechnical materials. In order to accurately predict the soil thermal resistivity，a prediction model of soil thermal resistivity is established based on the multivariate distribution model. The prediction model is verified，analyzed，and compared with the traditional empirical model to clarify the performance of the proposed model. The results show that the established multivariate distribution model can accurately predict the soil thermal resistivity. With the increase of input parameters，the prediction accuracy of multivariate distribution model is significantly improved，the is increased from 0.719 5 to 0.899 5，the value is reduced from 1.077 8 to 1.037，and the value is reduced from 0.367 5 to 0.212 1. The model has the best prediction accuracy. The prediction accuracy of multivariate distribution model is obviously better than that of traditional empirical model. For the prediction of thermal resistivity of different soil types with significant differences in engineering properties and complex sedimentary environment，it is suggested to select different types of multivariate distribution models according to the data to evaluate the thermal resistivity.

Key words： soil mechanics thermal resistivity normal distribution multivariate distribution model prediction model

引用本文:

王才进1，武猛1，蔡国军1，2，赵泽宁1，刘松玉1. 基于多元分布模型预测土体热阻系数[J]. 岩石力学与工程学报, 2023, 42(S1): 3674-3686.
WANG Caijin1，WU Meng1，CAI Guojun1，2，ZHAO Zening1，LIU Songyu1. Prediction of soil thermal resistivity based on multivariate distribution models. , 2023, 42(S1): 3674-3686.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2022.0643 或 http://rockmech.whrsm.ac.cn/CN/Y2023/V42/IS1/3674

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