【摘要】 地下开挖会导致应力重新分布形成松动圈。松动圈是影响地下开挖稳定性和支护设计的重要因素，准确预测松动圈厚度对于地下开挖工程至关重要。本研究通过模拟退火和贝叶斯优化方法优化XGBoost和随机森林(RF)算法，开发了四种新型的混合集成学习模型，即SA-XGBoost、SA-RF、BOXGBoost和BO-RF模型。首先，在某金矿和某铅锌矿共收集了210个松动圈样本，其中包括7个输入指标：埋深、巷道跨度、岩石单轴抗压强度、岩石质量等级、容重、巷道侧压力系数和炸药单耗。然后，通过R2、RMSE、MAE和VAF等4个评估指标对所提出模型的性能进行评估。结果表明：SAXGBoost模型的R2、RMSE、MAE和VAF值分别为0.924,0.157,0.117和0.924，在所有模型中表现最佳。最后，将所提出的四个混合集成学习模型在三个矿山进行工程应用，SA-XGBoost模型的松动圈预测平均误差最小，为5.06%。因此，该模型可被认为是估算地下巷道EDZ厚度的可行且高效的工具。更多还原

【Abstract】 Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ), which is an important factor affecting the excavation stability and support design. Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation. In this study, four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting(XGBoost) and random forest(RF) algorithms through simulated annealing(SA) and Bayesian optimization(BO) approaches, namely SA-XGBoost, SA-RF, BOXGBoost and BO-RF models. A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province, China, including seven input indicators: embedding depth, drift span, uniaxial compressive strength of rock, rock mass rating, unit weight of rock, lateral pressure coefficient of roadway and unit consumption of blasting explosive. The performance of the proposed models was evaluated by the coefficient of determination, root mean squared error, mean absolute error and variance accounted for. The results indicated that the SA-XGBoost model performed best. The Shapley additive explanations method revealed that the embedding depth was the most important indicator. Moreover, the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.更多还原