基于TBM掘进大数据和特征参数的引水隧洞塌方分析

Collapse Analysis in Water-Diversion Tunnels Based on Boring Big Data and Characteristic Parameters of Tunnel Boring Machine

针对TBM掘进过程中缺乏对围岩质量和塌方风险快捷、精准的预测预警方法,通过对TBM掘进大数据的深入挖掘,结合对实际工程塌方数据的剖析,提出潜在塌方风险的辅助判断依据。首先,对冗杂、连续的原始采集数据进行预处理,获取高质量的分析数据;然后,基于参数的相关性分析提出围岩特征参数的计算方法,并围绕特征参数的合理性和适用性,通过理论推导、室内试验和现场原位掘进试验进行论证;最后,结合实际的TBM塌方案例分析特征参数与围岩地质情况的相关性,提出塌方风险快速判断依据。结果表明:基于TBM掘进数据获取的围岩特征参数在一定程度上反映了围岩质量,其数值与围岩质量正相关,当其数值显著降低、变幅超过69.2%时,当前的掘进循环极大可能存在塌方风险。

There is a lack of prediction and early-warning methods for surrounding rock quality and collapse risk when using a tunnel boring machine(TBM).TBM boring big data are mined to address this,and the collapse data are analyzed to provide auxiliary judgment criteria for potential collapse risks.Redundant and continuous raw data are preprocessed to obtain high-quality analytical data.A method for calculating characteristic rock parameters based on parameter correlation analysis is proposed.The rationality and applicability of these parameters are demonstrated through simplified theoretical derivations,indoor test results,and on-site boring tests.Finally,based on actual TBM collapse cases,the correlation between the characteristic parameters and surrounding rock geological conditions is analyzed,providing a basis for the rapid assessment of collapse risk.The results show that the characteristic parameters of the surrounding rock obtained from processing and analysis of the TBM boring data reflect the quality of the surrounding rock,and their values are positively correlated with the quality of the surrounding rock.When their values significantly decrease and the variation exceeds 69.2%,the current boring cycle is highly prone to potential collapse risk.