| 摘要: |
| 为准确预测土体热阻系数，通过室内热探针测试与数据分析，简要分析了含水量、干密度、矿物成分和颗粒形态等因素对土体热传导特性的影响，利用人工神经网络(ANN)技术，建立了计算土体热阻系数的预测模型，并与传统经验关系模型进行对比，明确所提计算模型的可靠性与优越性．结果表明：土体传热性能受众多因素影响，其热阻系数难以准确估算，基于ANN的计算模型可以较好地解决这一问题；以含水量和干密度为输入参数的单个模型适用于特定类型土体，而4个输入参数（含水量、干密度、黏粒含量和石英含量）的广义模型不受此限制，增加相关输入参数可有效保证模型计算结果的精确度；单个模型和广义模型的计算结果与实测结果吻合良好，预测能力均显著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，建议优先选用广义模型来估算其热阻系数. |
| 关键词: 岩土材料 热阻系数 人工神经网络 预测模型 经验公式 |
| DOI：103969/j.issn1007 9629202002021 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(41807260);中央高校基本科研业务费专项资金资助项目(CUG170636,CUGL170807) |
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| Prediction Model of Thermal Resistivity of GeomaterialBased on Artificial Neural Network |
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ZHANG Tao1, WANG Caijin1, LIU Songyu2, DUAN Longchen1
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1.Faculty of Engineering, China University of Geosciences, Wuhan 430074, China;2.Institute of Geotechnical Engineering, Southeast University, Nanjing 210096, China
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| Abstract: |
| To predict thermal resistivity accurately, the thermal probe testing in laboratory and data analysis were conducted to briefly investigate the effects of moisture content, dry density, mineralogy, and soil particle morphology on thermal conduction of soils. Based on the artificial neural network(ANN), the prediction models for estimating soil thermal resistivity were proposed. A comparison between models proposed and traditional empirical models was made to verify the reliability and superiority of the ANN models. The analytical results reveal that soil heat transfer performance is influenced by many factors related which would make it difficult to estimate its thermal resistivity. The proposed models based on artificial neural network make it possible to solve this problem well. The ANN individual models including two input parameters(i.e., moisture content and dry density) are suitable for a given type of soil, while the generalized model with four input parameters(i.e., moisture content, dry density, clay content, and quartz content) is not subject to the restriction of soil type. The increment in input parameter can effectively keep the accuracy of the prediction models of their calculation results. The predicted thermal resistivity values from both ANN individual models and generalized model is in good agreement with the measured ones, and the prediction performance of these two models is much better than that of the traditional empirical models. It is shown that the generalized model is preferred in estimation of thermal resistivity of various types of soils which are in obvious discrepancy in engineering properties and depositional environments. |
| Key words: geomaterial thermal resistivity artificial neural network(ANN) prediction model empirical equation |
