Yuxin Chen1, Weixun Yong1, Chuanqi Li2, Jian Zhou1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714
Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress
and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key
basis for roadway stability discrimination and support structure design, and it is of great engineering significance
to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with
high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is
developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters… More >
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