Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes
Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes
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应用多种机器学习方法预测稳定池塘灰烬的无侧限抗压强度
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发表时间：
2019
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通讯作者：
Manju Suthar
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作者：
Manju Suthar
本文评估了五种建模方法的潜力，即M5模型树，随机森林，人工神经网络，支持向量机和高斯过程，用于预测石灰和石灰渣稳定化池灰的无侧限抗压强度。该研究不仅为同一组数据提供了五种模型，还比较了它们的整体性能。使用的数据集由从实验室实验中获得的255个样本组成。其中，170个随机选择的样本用于训练，其余85个用于测试模型。输入数据集由8个参数（均匀系数、曲率系数、最大干密度、最佳含水量、石灰、石灰渣、养护期和7天浸泡加州承载比）组成，而输出是养护7、28、45、90和180天的UCS值。结果表明，高斯过程建模策略效果良好，整体性能更接近精确的协议线。GP模型的检验结果表明，该数据集的CC值较高，为0.997，RMSE值较低，为23.016kPa，MAE值较低，为16.455。敏感性分析表明，石灰、石灰渣、养护周期和加州承载比是预测稳定化池灰无侧限抗压强度的重要参数。结果证实，GP模型是在一个位置，以预测无侧限抗压强度的稳定化池灰的准确度过高，然而，GP建模方法证明，这种方法是更经济，更容易在繁琐的实验室工作相比。
This paper evaluates the potential of five modeling approaches, namely M5 model tree, random forest, artificial neural networks, support vector machines and Gaussian processes, for the prediction of unconfined compressive strength of stabilized pond ashes with lime and lime sludge. The study not only presents five models for the same set of data but also compares the overall performance of them. Dataset used consists of 255 samples acquired from laboratory experiments. Out of the total, 170 randomly chosen samples were used for training and remaining 85 were used for testing the models. Input dataset consists of eight parameters (uniformity coefficient, coefficient of curvature, maximum dry density, optimum moisture content, lime, lime sludge, curing period and 7-day soaked California bearing ratio), while the output is UCS value at 7, 28, 45, 90 and 180 days of curing. Comparisons of results propose that Gaussian processes modeling strategy works well and the overall performance was substantially nearer to the exact agreement line. As a result of GP model, higher value of CC = 0.997 and lower values of RMSE = 23.016 kPa and MAE = 16.455 were obtained for testing the dataset. Sensitivity analysis suggests that lime, lime sludge, curing period and California bearing ratio are the significant parameters for predicting the unconfined compressive strength of stabilized pond ashes. The results confirmed that GP models are in a position to predict the unconfined compressive strength of stabilized pond ashes with an excessive degree of accuracy; however, GP modeling approach proves that this approach is more economical and less difficult in comparison with tedious laboratory work.