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

Abstract

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).

The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

“…The RF model is mainly applied to the fields of classification and regression. As a nonlinear modeling algorithm, the RF regression model is widely used in data mining [ 56 ], bioinformatics statistics [ 57 , 58 ], and other fields. Many studies have proved that RF regression has very high prediction accuracy, and it is less affected by noise and outliers, so it is not easy to overfit [ 59 ].…”

Evaluation of Applicability of 1D-CNN and LSTM to Predict Horizontal Displacement of Retaining Wall According to Excavation Work

“…An artificial neural network (ANN) was used in many research [5][6][7][8][9][10][11][12][13][14][15][16] to estimate the lateral wall displacement in excavation works. As some research trend, ANN was also used by Kung et al [11] to calculate the deflection of diaphragm walls caused by excavation in clays.…”

“…www.ijacsa.thesai.org model. Xie and Peng [16] tested the prediction power of Random Forest (RF) modeling for estimating tunnel Excavation Damaged Zones (EDZs). Despite the widespread application of supervised learning algorithms in geotechnical engineering, they have not been frequently applied for lateral wall displacement prediction in deep braced excavations considering the anisotropic shear strength.…”

Parametric Evaluation of Simultaneous Effects of Damaged Zone Parameters and Rock Strength Properties on GRC

“…True characterization of this blast/ excavated induced damage zone (BIDZ/EIDZ) is carried out based on the deterioration of material strength parameters, consideration of the variation of Young's modulus, and the weight of this zone into the problem. e effect of EDZ has been studied numerously [24][25][26][27][28][29][30][31][32][33][34][35][36]. However, there is limited, available solutions, which consider the effects of both softening material behavior in the plastic zone and the existence of BIDZ/EIDZ around the tunnel [26,[37][38][39][40].…”