Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes

Summary This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines....

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Bibliographic Details
Published in:International journal for numerical and analytical methods in geomechanics Vol. 43; no. 4; pp. 801 - 813
Main Authors: Sun, Yuantian, Zhang, Junfei, Li, Guichen, Wang, Yuhang, Sun, Junbo, Jiang, Chao
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.03.2019
Subjects:
Antennae
Architecture
Artificial neural networks
Back propagation networks
back propagation neural network
beetle antennae search algorithm
Cement
Coal
Coal mines
Compressive strength
Concrete
Curing
Curing (processing)
Grout
Grouting
Jet grouting
jet grouting coalcrete
Mathematical models
Modelling
Neural networks
prediction
Predictions
Regression analysis
Sensitivity analysis
Support vector machines
unconfined compressive strength
Water-cement ratio
ISSN:0363-9061, 1096-9853
Online Access:Get full text
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Summary:Summary This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction.
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ISSN:0363-9061
1096-9853
DOI:10.1002/nag.2891