Citation: Zhao, G.; Wang, M.; Liang,
W. A Comparative Study of
SSA-BPNN, SSA-ENN, and SSA-SVR
Models for Predicting the Thickness
of an Excavation Damaged Zone
around the Roadway in Rock.
Mathematics 2022, 10, 1351. https://
doi.org/10.3390/math10081351
Academic Editors: Nikos D. Lagaros
and Vagelis Plevris
Received: 7 March 2022
Accepted: 16 April 2022
Published: 18 April 2022
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Article
A Comparative Study of SSA-BPNN, SSA-ENN, and SSA-SVR
Models for Predicting the Thickness of an Excavation Damaged
Zone around the Roadway in Rock
Guoyan Zhao , Meng Wang and Weizhang Liang *
School of Resources and Safety Engineering, Central South University, Changsha 410083, China;
gyzhao@csu.edu.cn (G.Z.); mwanglh@csu.edu.cn (M.W.)
* Correspondence: wzlian@csu.edu.cn
Abstract:
Due to the disturbance effect of excavation, the original stress is redistributed, resulting
in an excavation damaged zone around the roadway. It is significant to predict the thickness of
an excavation damaged zone because it directly affects the stability of roadways. This study used
a sparrow search algorithm to improve a backpropagation neural network, and an Elman neural
network and support vector regression models to predict the thickness of an excavation damaged
zone. Firstly, 209 cases with four indicators were collected from 34 mines. Then, the sparrow search
algorithm was used to optimize the parameters of the backpropagation neural network, Elman neural
network, and support vector regression models. According to the optimal parameters, these three
predictive models were established based on the training set (80% of the data). Finally, the test set
(20% of the data) was used to verify the reliability of each model. The mean absolute error, coefficient
of determination, Nash–Sutcliffe efficiency coefficient, mean absolute percentage error, Theil’s U
value, root-mean-square error, and the sum of squares error were used to evaluate the predictive
performance. The results showed that the sparrow search algorithm improved the predictive per-
formance of the traditional backpropagation neural network, Elman neural network, and support
vector regression models, and the sparrow search algorithm–backpropagation neural network model
had the best comprehensive prediction performance. The mean absolute error, coefficient of de-
termination, Nash–Sutcliffe efficiency coefficient, mean absolute percentage error, Theil’s U value,
root-mean-square error, and sum of squares error of the sparrow search algorithm–backpropagation
neural network model were 0.1246, 0.9277,
−
1.2331, 8.4127%, 0.0084, 0.1636, and 1.1241, respectively.
The proposed model could provide a reliable reference for the thickness prediction of an excavation
damaged zone, and was helpful in the risk management of roadway stability.
Keywords:
excavation damaged zone; prediction; sparrow search algorithm; BP neural network;
Elman neural network; support vector regression
MSC: 86-10
1. Introduction
After the excavation of roadway, the initial stress in the surrounding rock mass is
redistributed. When the stress is greater than the strength of the surrounding rock, the rock
mass will be damaged. Then, a ringlike broken zone can be formed around the excavated
space; this is called the excavation damaged zone (EDZ) [
1
,
2
]. The thickness of the EDZ
can not only be used to judge the stability of the roadway, but can also be adopted in the
support design [
3
–
5
]. In addition, due to the weakening in the rock strength, an EDZ can
also be utilized for nonexplosive continuous mining in deep hard-rock mines [
6
]. Therefore,
predicting the thickness of the EDZ around a roadway is significant.
Since the concept of the EDZ was proposed, many scholars have conducted plenty of
research to determine its size or thickness. These methods can be mainly summarized as the
Mathematics 2022, 10, 1351. https://doi.org/10.3390/math10081351 https://www.mdpi.com/journal/mathematics
