围岩分级是隧道工程建设中的核心要素。随着中国机械化和智能化建造技术的迅速发展，基于随钻参数的围岩智能分级方法已成为推动隧道智能化机械施工的关键保障。尤其在西部山区，由于地形复杂、施工难度大，再加上山区隧道机械化施工经验不足，且现有智能围岩分级方法实用性有限，机械化建造对提升工程质量和有效控制施工风险至关重要。提出一种基于随钻测量参数的智能隧道围岩分级预测方法。以多条特长隧道为研究对象，采集现场随钻参数并进行围岩力学性能测试，构建随钻参数样本库，并通过支持向量回归（support vector regression, SVR）和粒子群优化-反向传播（particle swarm optimization-back propagation, PSO-BP）智能算法构建围岩智能分级预测模型。研究表明，随钻参数与围岩分级指标的相关系数绝对值|r_s|>0.6，显示出显著相关性，其中扭矩和旋转速度与围岩分级指标的相关性最强。使用数据清洗工具建立了包含574份理想样本的标准化参数指标数据库。对比分析智能算法的预测精度，结果表明PSO-BP预测模型的表现最佳。经瞬变电磁（transient electromagnetic, TEM）和隧道地震（tunnel seismic prediction, TSP）超前地质预报验证，基于PSO-BP神经网络的围岩智能分级预测模型能够准确预测围岩分级，为隧道机械化开挖提供可靠的分级支持。

Rock mass classification is a fundamental component in tunnel engineering construction. With the rapid advancement of mechanized and intelligent construction technologies in China, drilling-parameter-based intelligent rock mass classification methods have become crucial in facilitating smart mechanized tunneling. This need is especially pronounced in the mountainous regions of Western China, where complex terrain and challenging construction, combined with limited experience in mechanized tunneling and the restricted applicability of current intelligent rock mass classification methods, make mechanized construction crucial for improving project quality and effectively controlling construction risks. A predictive method was proposed for intelligent rock mass classification using drilling measurement parameters. Focusing on multiple long tunnels as research subjects, on-site drilling parameters were collected and rock mass mechanical tests was conducted to construct a drilling parameter database, then intelligent algorithms was applied, such as support vector regression (SVR) and particle swarm optimization-back propagation (PSO-BP), to develop a predictive model for rock mass classification. The result indicates that the absolute value of correlation coefficient | r_s | between drilling parameters and rock mass classification indices is greater than 0.6, demonstrating a significant correlation, where torque and rotational speed show the strongest correlation with rock mass classification indices. A standardized parameter index database with 574 ideal samples was established through data-cleaning tools. Comparative analysis of predictive accuracy across intelligent algorithms indicated that the PSO-BP model demonstrated the best performance. The PSO-BP neural network-based prediction model was validated by transient electromagnetic(TEM) and tunnel seismic prediction (TSP) advanced geological forecasting, confirming its accuracy in predicting rock mass classification and providing reliable support for mechanized tunnel excavation.