基于混合模型的煤矸石透水混凝土透水系数预测

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Abstract In order to improve the prediction accuracy of permeability coefficient of coal gangue permeable concrete, this work proposes a hybrid model prediction method based on ensemble learning. The model combines BP neural network, XGBoost and support vector machine, solves the problems of local optimum, underfitting, overfitting and poor generalization ability of dependent data set when using a single prediction model to predict, and further improves the accuracy of prediction. The test results of different types of data sets show that the model can effectively reduce the training error generated by a single model and improve the robustness of the model, which has high application value.

Key words： neural network machine learning ensemble learning prediction of permeability coefficient of coal gangue concrete

Published: 05 June 2022 Online: 2022-06-08

CLC number:

TU528

Fund:Undergraduate Training Program for Innovation and Entrepreneurship, China University of Mining and Techonology(202110290019Z).

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	Articles by authors
	DU Qingxuan
	ZHANG Yuhang
	SUN Weihao
	LIU Rui
	ZHUANG Yaoliang
	XIA Junwu

Cite this article:

DU Qingxuan,ZHANG Yuhang,SUN Weihao, et al. Prediction of Permeability Coefficient of Coal Gangue Permeable Concrete Based on Mixed Model[J]. Materials Reports, 2022, 36(Z1): 22040077-5.

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http://www.mater-rep.com/EN/ OR http://www.mater-rep.com/EN/Y2022/V36/IZ1/22040077

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