为解决大跨度暗挖车站拱盖法施工支护参数优化设计中，传统方法依赖反复迭代数值模拟、寻优效率低下的难题，提出一种基于数据驱动的支护参数智能优选框架。首先，针对上软下硬典型地层拱盖法施工车站，通过拉丁超立方抽样结合地质参数耦合逻辑规则，并利用Python与FLAC3D协同自动化计算，构建首个专用的工程参数数据库；随后，在对比多种机器学习算法后，选取预测性能最优的XGBoost作为基础位移预测模型，并采用四参数自适应生长优化器（QAGO）对其进行超参数精细调优，显著提升模型预测精度；最后，为克服单一元启发式算法寻优易陷入局部最优或结果过于保守的局限性，提出一种融合黏菌算法（SMA）与蜜獾算法（HBA）的混合优化策略，并引入支护经济性作为优化目标。研究结果表明： 1）所提出的系统化数据库构建流程能够高效生成符合工程逻辑的数据集； 2）经QAGO调优后的XGBoost模型为支护参数优化提供了可靠的代理模型； 3）基于SMA-HBA混合算法的集成模型能够快速寻得满足安全约束且更具经济性的支护方案。
Traditional support parameter optimization for large-span excavated stations constructed using the arch-cover method relies on repetitive numerical simulations, resulting in low efficiency. To address this limitation, a data-driven intelligent optimization framework is proposed. First, a case study of a metro station constructed using the arch-cover method in upper-soft and lower-hard strata is conducted, and a specialized engineering parameter database is established based on Latin hypercube sampling and logical coupling rules for geological parameters, supported by cooperative automated computation between Python and FLAC3D. Subsequently, multiple machine learning algorithms are evaluated, and extreme gradient boosting (XGBoost) is selected as the displacement prediction model because of its superior predictive performance. The model hyperparameters are further optimized using a four-parameter adaptive growth optimizer (QAGO), which significantly improves prediction accuracy. Finally, to overcome the limitations of single metaheuristic algorithms, including susceptibility to local optimal and overly conservative solutions, a hybrid optimization strategy integrating the slime mold algorithm (SMA) and the honey badger algorithm (HBA) is developed, with support economy incorporated as an optimization objective. The results indicate that (1) the proposed database generation method efficiently produces datasets that conform to engineering logic, (2) the QAGO-optimized XGBoost model provides a reliable surrogate model for support parameter optimization, and (3) the SMA-HBA hybrid optimization framework rapidly identifies support schemes that satisfy safety constraints while achieving improved economic performance.
