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

Yuantian Sun, Junfei Zhang, Guichen Li, Yuhang Wang, Junbo Sun, Chao Jiang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)801-813
Number of pages13
JournalInternational Journal for Numerical and Analytical Methods in Geomechanics
Volume43
Issue number4
DOIs
Publication statusPublished - Mar 2019

Cite this

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title = "Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes",
abstract = "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.",
keywords = "back propagation neural network, beetle antennae search algorithm, jet grouting coalcrete, prediction, unconfined compressive strength, MECHANICAL-PROPERTIES, ULTRASONIC PROPERTIES, COLUMNS, MODULUS, SYSTEM",
author = "Yuantian Sun and Junfei Zhang and Guichen Li and Yuhang Wang and Junbo Sun and Chao Jiang",
year = "2019",
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pages = "801--813",
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Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes. / Sun, Yuantian; Zhang, Junfei; Li, Guichen; Wang, Yuhang; Sun, Junbo; Jiang, Chao.

In: International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 43, No. 4, 03.2019, p. 801-813.

Research output: Contribution to journalArticle

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

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DO - 10.1002/nag.2891

M3 - Article

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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 -