Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/20291
Nhan đề: Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression
Tác giả: Xie, Qiang
Peng, Kang
Từ khoá: Space-Time Distribution Laws
Civil Engineering
Random Forest Regression
Deep Mines
Tunnel Excavation Damaged Zones (EDZs)
EDZ Prediction Modeling
Năm xuất bản: 2019
Nhà xuất bản: Hindawi Limited
Tóm tắt: 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 rockin deep engineering and determining a reasonable support scheme. In a layered rockmass, the distributionofEDZsismoredifficulttoidentify.Inthisstudy,anultrasonicvelocitydetectorinthesurroundingrockwasusedto monitortherangeofEDZsinadeeproadwaywhichwasburiedinalayeredrockmasswithadipangleof20–30°.Thespace-time distribution laws of the range of EDZs during the excavation process of the roadway were analyzed. The monitoring results showedthattheformationofanEDZcanbedividedintothefollowingstages:(1)theEDZformsimmediatelyaftertheroadway excavation,whichaccountsforapproximately82%–95%ofallEDZs.ThemainfactorsthataffecttheEDZaretheblastingload,the excavationunloading,andthestressadjustment (2)astheroadwayexcavationcontinues,therangeoftheEDZsincreasesbecause 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 meanabsoluteerror(MAE)areusedasreliableindicatorstovalidatethemodel.Theresultsindicatethattherandomforestmodel has good prediction capability (RMSE�0.1613 and MAE�0.1402).
Định danh: http://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/20291
ISSN: 1687-8086
1687-8094 (eISSN)
Bộ sưu tập: Bài báo_lưu trữ

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