TY - JOUR
T1 - Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes
AU - Sun, Yuantian
AU - Zhang, Junfei
AU - Li, Guichen
AU - Wang, Yuhang
AU - Sun, Junbo
AU - Jiang, Chao
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - back propagation neural network
KW - beetle antennae search algorithm
KW - jet grouting coalcrete
KW - prediction
KW - unconfined compressive strength
KW - MECHANICAL-PROPERTIES
KW - ULTRASONIC PROPERTIES
KW - COLUMNS
KW - MODULUS
KW - SYSTEM
U2 - 10.1002/nag.2891
DO - 10.1002/nag.2891
M3 - Article
VL - 43
SP - 801
EP - 813
JO - International Journal of Numerical and Analytical Methods in Geomechanics
JF - International Journal of Numerical and Analytical Methods in Geomechanics
SN - 0363-9061
IS - 4
ER -