高岭土的力学性能及其应用研究

发布时间：2018-01-24 07:45

本文关键词： 高岭土第一性原理计算弹性常数 BP神经网络抗碳化　出处：《广西科技大学》2015年硕士论文　论文类型：学位论文

【摘要】：本文运用第一性原理计算方法和BP神经网络,研究了高岭土的力学性能以及高岭土在土木工程中有关的应用。主要研究工作及结论如下:(1)运用第一性原理计算方法计算了高岭土在不同压力作用下的弹性常数、体积、总能、键长的变化,研究了高岭土的微观力学性能。通过计算,高岭土小的压力作用下其体积、总能、键长基本无变化,而弹性常数结构优化前后变化很大。(2)运用BP神经网络对影响沉降因素的归类,训练、模拟,建立了影响因素与沉降量之间的非线性关系,通过模拟得到预测值与实测值之间的误差很小,BP神经网络方法能成为有效预测膨胀性高岭土质粘土地基沉降数据的方法。(3)为了研究混凝土的抗碳化性,对高岭土进行剥片处理并将其作为掺合料进行碳化试验,结果表明:随着剥片后的偏高岭土掺量的增加,混凝土的抗碳化能力也相应的提高了,且当矿物掺合料总掺量为35%,偏高岭土掺量为15%时,混凝土抗碳化能力最大的提高率达到38.02%。实验结果可为提高混凝土抗碳化性能提供一种新的思路。
[Abstract]:The first principle calculation method and BP neural network are used in this paper. The mechanical properties of kaolin and its application in civil engineering are studied. The main work and conclusions are as follows: 1). The elastic constants of kaolin under different pressures were calculated by first-principle method. The change of volume, total energy and bond length, the micromechanical properties of kaolin are studied. The volume, total energy and bond length of kaolin under small pressure have no change. And the elastic constant structure before and after optimization is very big change.) the BP neural network is used to classify, train, simulate, and establish the nonlinear relationship between the influence factors and the settlement amount. The error between the predicted value and the measured value is very small by simulation. BP neural network method can be used to predict the settlement data of expansive kaolin clay foundation effectively.) in order to study the carbonation resistance of concrete. The results show that with the increase of the content of metakaolin after stripping, the carbonization resistance of concrete is improved accordingly. And when the total amount of mineral admixture is 35% and the content of metakaolin is 15%. The maximum increase rate of carbonation resistance of concrete is 38.02. The experimental results can provide a new way to improve the carbonation resistance of concrete.
【学位授予单位】：广西科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU528

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