Application of Machine Learning in the Prediction of Compressive Strength of Concrete
炜东 饶 · Statistics and Applications · 2017
本文对混凝土的抗压强度的数据采用决策树、Boosting、随机森林、人工神经网络、支持向量机这五种方法进行建模，采用十折交叉验证评价预测精度。发现随机森林法具有较好的预测效果。 In this paper, the data of compressive strength of concrete are modeled by decision tree, boosting, random forest, artificial neural network and support vector machine methods. Ten-fold cross-va- lidation is adopted to assess the performance of these methods in terms of the prediction accuracy. It is seen that the Random Forest method has the best performance in general.