Xinjiang University School of Civil Engineering
National Natural Science Foundation of China Youth Science Fund Project；Xinjiang Uygur Autonomous Region University Scientific Research Project
位于我国西北部的东天山地区，拥有着复杂的地质条件，因此如何快速准确预测隧道掌子面前方的围岩质量的难度增大，有一种能准确客观反映岩体基本特性的围岩分类，是隧道设计与施工的重要参考依据。本文旨在建立一种能客观准确评价东天山地区工程地质环境及预测围岩等级的方法，依托在建东天山隧道项目，选取东天山特长隧道已开挖典型地质区段，以工程地质分区、高关联度物探技术参数指标及物探偏移图像为基础，组成机器学习训练样本；并采用 Python 语言基于TensorFlow深度学习框架编写深度学习网络算法训练样本，建立围岩类别预测模型，并采用新开挖段数据不断验证与优化模型，最后将预测精度最高的模型推广应用于天山地区隧道围岩类别预测，结果表明用TST偏移图像+地质分区+物探指标数据集训练出来的模型效果最好。
The East Tianshan region, located in the northwest of China, has complex geological conditions. Therefore, it is more difficult to quickly and accurately predict the quality of surrounding rock in front of the tunnel face. There is a surrounding rock classification that can accurately and objectively reflect the basic characteristics of rock mass, which is an important reference basis for tunncel design and construction. In order to establish a method that can objectively and accurately evaluate the engineering geological environment and predict the grade of surrounding rock in the East Tianshan area, the East Tianshan Tunnel under construction is selected as the support project of this paper, and the typical geological section of the East Tianshan extra-long tunnel that has been excavated is selected. Based on the engineering geological zoning, the technical parameters of high correlation geophysical prospecting and the geophysical migration image, the machine learning training samples are composed. The training samples of deep learning network algorithm are compiled by Python language based on TensorFlow deep learning framework, and the prediction model of surrounding rock category is established. The new excavation section data are used to continuously verify and optimize the model. Finally, the model with the highest prediction accuracy is applied to the prediction of surrounding rock category of tunnels in Tianshan area. The results show that the model trained by TST offset image + geological zoning + geophysical prospecting index data set has the best effect.
孟馨,秦拥军,谢良甫,等. 基于深度学习的东天山特产隧道围岩等级预测[J]. 科学技术与工程, , ():复制