施工-地质双驱动的地下洞室有害气体浓度智能预测方法

Intelligent prediction method of harmful gas concentrations in underground caverns driven by construction-geological joint impacts

地下洞室有害气体浓度与开挖方案、地质条件等密切相关,开展施工-地质影响下的有害气体浓度精准预测对于施工安全管理具有重要意义。然而,有害气体监测数据有用信息难被提取,爆破参数、基岩类别等特征与有害气体浓度间存在非线性耦合,基于此,本研究提出融合沙普利加性解释(SHAP)理论的集成学习有害气体浓度智能预测方法。通过主成分分析(PCA)进行特征预处理,运用树结构贝叶斯优化(TPE)算法迭代寻求CatBoost有害气体浓度预测模型最优超参数组,引入SHAP解释框架,探寻有害气体排放浓度重要影响因子。以金沙江旭龙水电站导流洞工程为例,研究结果表明:对比CatBoost、TPE-XGBoost以及TPE-LightGBM模型,TPE-CatBoost模型均方根误差(RMSE)分别降低了48.9%、40.2%、36.8%,具有更高预测精度;融合SHAP理论发现PM_(10)、PM_(2.5)浓度与爆破方案关联更为密切,CO、CO_(2)浓度受地下水状态等地质条件影响更大。

The concentration of harmful gases in underground caverns is closely related to excavation schemes and geological conditions;its accurate prediction under construction-geological impacts is crucial to construction safety management.However,it is challenging to extract useful information from harmful gas monitoring data due to the nonlinear coupling between features such as blasting parameters,bedrock types,and gas concentrations.This study presents an intelligent prediction method for harmful gas concentrations using integrated learning,incorporating the theory of SHapley Additive exPlanations(SHAP).The method conducts feature preprocessing using Principal Component Analysis(PCA),and uses the Tree-structured Parzen Estimator(TPE)algorithm to iteratively seek the optimal hyperparameters for the CatBoost(Categorical Boosting)model that is used to predict harmful gas concentrations.The SHAP explanatory framework is introduced to identify key factors affecting gas emission concentrations.Application in a case study of the diversion tunnel at the Xulong hydropower station shows that compared with the models of CatBoost,TPE-XGBoost,and TPE-LightGBM,the TPE-CatBoost model reduces the root mean square error by 48.9%,40.2%,and 36.8%respectively,demonstrating a higher prediction accuracy.Integrating the SHAP theory reveals that PM_(10)and PM_(2.5)concentrations are more closely associated with blasting schemes,while CO and CO_(2)concentrations are more influenced by geological conditions such as groundwater state.