基于RBF神经网络的土体导热系数非线性预测
Acta Energiae Solaris Sinica（2021）
Abstract
土体导热系数对于浅层地热能、地下空间等的开发利用和地层储能、核废料处置等的设计具有重要意义.在前人研究的基础上,对于同一地区同类型的土体,选取土体中固体、液体和气体的体积分数作为自变量,导热系数为因变量,利用SPSS软件RBF神经网络进行非线性预测.以在文献中收集的红黏土、粉土、高压实膨润土等数据进行计算分析,验证了该方法的优越性.然后用该方法对北京平原区浅层地热能资源地质勘查项目和杭州市萧山区浅层地热能资源调查评价项目中的数据进行了计算和预测,预测结果与实测值的平均相对误差均小于5％.
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