基于GA-LightGBM算法的TBM掘进参数与岩体等级关系

Relationship between TBM tunneling parameters and rock mass grades based on GA-LightGBM algorithm

摘要:

为建立反映TBM掘进参数与岩体等级关系的岩体等级识别模型,并提高模型的构建效率和识别率,以某隧道工程为背景开展研究。现场勘测岩体特征并基于BQ法和RQD值划分岩体等级,采集TBM工作数据并筛选出与岩体特性变化相关的主要掘进参数；基于轻量梯度提升器(LightGBM)算法,拟合TBM掘进参数与岩体等级的关系,并利用遗传算法(GA),优化LightGBM的超参数,最终建立GA-LightGBM岩体等级识别模型。结果表明:GA-LightGBM模型的识别准确率达到了93.5%,高于支持向量机模型和随机森林模型的准确率,且模型训练速度比梯度提升决策树算法提高了8倍；5种TBM掘进参数与岩石强度和岩体完整性等特性存在相关关系,其中总推进力可作为感知岩体特征的主要判据。研究提供了一种高效分析TBM掘进参数并准确识别岩体等级的方法,为现场快速感知岩体等级并实时调整作业参数提供支撑。

Abstract:

In order to establish an identification model of rock mass grade reflecting the relationship between TBM tunneling parameters and rock mass categories, improve model building efficiency and recognition rate, a research was carried out on background of a tunnel project. The rock mass characteristics were surveyed and graded based on BQ method and RQD, the TBM working data was collected and the main excavation parameters related to the change of rock mass characteristics were screened. The relationship between TBM tunneling parameters and rock mass grade was fitted based on Light Gradient Boosting Machine (LightGBM) algorithm, and the hyperparameters of LightGBM were optimized using genetic algorithm (GA), then a GA-LightGBM model of rock mass grades identification was established. Results: The accuracy of the GA-LightGBM recognition model reached 93.5%, which was higher than that of the support vector machine model and the random forest model. The model training speed is 8 times faster than the gradient boosting decision tree algorithm. Five TBM tunneling parameters were related to rock strength and rock mass integrity, and the total propulsion force could be used as the main criterion for sensing rock mass characteristics. The study provides an efficient method for analyzing TBM excavation parameters and accurately identifying rock mass grades, providing support for rapid on-site perception of rock mass grades and real-time adjustment of operating parameters.