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

Joint Authors

Peng, Kang
Xie, Qiang

Source

Advances in Civil Engineering

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-05-29

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Civil Engineering

Abstract EN

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).

American Psychological Association (APA)

Xie, Qiang& Peng, Kang. 2019. Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression. Advances in Civil Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1116743

Modern Language Association (MLA)

Xie, Qiang& Peng, Kang. Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression. Advances in Civil Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1116743

American Medical Association (AMA)

Xie, Qiang& Peng, Kang. Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression. Advances in Civil Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1116743

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1116743