Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression

• Main Authors: Qiang Xie, Kang Peng
• Format: Article
• Language: English
• Published: Hindawi Limited 2019-01-01
• Series: Advances in Civil Engineering
• Online Access: http://dx.doi.org/10.1155/2019/6505984

The formation process of EDZs (excavation damaged zones) in the roadways of deep underground mines is complex, and this process is affected by blasting disturbances, engineering excavation unloading, and adjustment of field stress. The range of an excavation damaged zone (EDZ) changes as the time and space change. These changes bring more difficulties in analyzing the stability of the surrounding rock in deep engineering and determining a reasonable support scheme. In a layered rock mass, the distribution of EDZs is more difficult to identify. In this study, an ultrasonic velocity detector in the surrounding rock was used to monitor the range of EDZs in a deep roadway which was buried in a layered rock mass with a dip angle of 20–30°. The space-time distribution laws of the range of EDZs during the excavation process of the roadway were analyzed. The monitoring results showed that the formation of an EDZ can be divided into the following stages: (1) the EDZ forms immediately after the roadway excavation, which accounts for approximately 82%–95% of all EDZs. The main factors that affect the EDZ are the blasting load, the excavation unloading, and the stress adjustment; (2) as the roadway excavation continues, the range of the EDZs increases because of the blasting excavation and stress adjustment; (3) the later excavation zone has a comparatively larger EDZ value; and (4) an asymmetric supporting technology is necessary to ensure the stability of roadways buried in layered rocks. Additionally, the predictive capability of random forest modeling is evaluated for estimating the EDZ. The root-mean-square error (RMSE) and mean absolute error (MAE) are used as reliable indicators to validate the model. The results indicate that the random forest model has good prediction capability (RMSE = 0.1613 and MAE = 0.1402).