Abstract
The coalcrete, a new supporting material produced by jet grouting (JG) technique was firstly studied for improving soft coal mass to support roadways in Guobei coal mine, China. Young's modulus is an essential indicator to evaluate the deformation-resisting ability of coalcretes. In this study, for determining Young's modulus of coalcretes efficiently, an intelligent technique was proposed using the support vector machine (SVM) and beetle antennae search (BAS). The hyper-parameters of SVM were firstly tuned by BAS, and then the SVM-BAS model with optimum hyper-parameters was employed to model the non-linear relationship between the inputs (coal content, water content, cement content, and curing time) and output (Young's modulus). By combining these variables, 360 coalcrete samples in total were prepared and tested for establishing the dataset. The results show that BAS is more reliable and efficient than the trial–and–error tuning method. Moreover, by comparison with other baseline models such as back-propagation neural network (BPNN), logistic regression (LR) and multiple linear regression (MLR), the optimized SVM-BAS model is more reliable, accurate and less time consuming for predicting Young's modulus of coalcretes. Besides, by conducting sensitivity analysis (SA), the importance of different input variables was determined. This pioneering work provides guidelines for predicting Young's modulus of coalcretes and designing proper JG parameters in engineering applications.
| Original language | English |
|---|---|
| Pages (from-to) | 43-53 |
| Number of pages | 11 |
| Journal | Engineering Geology |
| Volume | 252 |
| DOIs | |
| Publication status | Published - 26 Mar 2019 |
Fingerprint
Cite this
}
Determination of Young's modulus of jet grouted coalcretes using an intelligent model. / Sun, Yuantian; Zhang, Junfei; Li, Guichen; Ma, Guowei; Huang, Yimiao; Sun, Junbo; Wang, Yuhang; Nener, Brett.
In: Engineering Geology, Vol. 252, 26.03.2019, p. 43-53.Research output: Contribution to journal › Article
TY - JOUR
T1 - Determination of Young's modulus of jet grouted coalcretes using an intelligent model
AU - Sun, Yuantian
AU - Zhang, Junfei
AU - Li, Guichen
AU - Ma, Guowei
AU - Huang, Yimiao
AU - Sun, Junbo
AU - Wang, Yuhang
AU - Nener, Brett
PY - 2019/3/26
Y1 - 2019/3/26
N2 - The coalcrete, a new supporting material produced by jet grouting (JG) technique was firstly studied for improving soft coal mass to support roadways in Guobei coal mine, China. Young's modulus is an essential indicator to evaluate the deformation-resisting ability of coalcretes. In this study, for determining Young's modulus of coalcretes efficiently, an intelligent technique was proposed using the support vector machine (SVM) and beetle antennae search (BAS). The hyper-parameters of SVM were firstly tuned by BAS, and then the SVM-BAS model with optimum hyper-parameters was employed to model the non-linear relationship between the inputs (coal content, water content, cement content, and curing time) and output (Young's modulus). By combining these variables, 360 coalcrete samples in total were prepared and tested for establishing the dataset. The results show that BAS is more reliable and efficient than the trial–and–error tuning method. Moreover, by comparison with other baseline models such as back-propagation neural network (BPNN), logistic regression (LR) and multiple linear regression (MLR), the optimized SVM-BAS model is more reliable, accurate and less time consuming for predicting Young's modulus of coalcretes. Besides, by conducting sensitivity analysis (SA), the importance of different input variables was determined. This pioneering work provides guidelines for predicting Young's modulus of coalcretes and designing proper JG parameters in engineering applications.
AB - The coalcrete, a new supporting material produced by jet grouting (JG) technique was firstly studied for improving soft coal mass to support roadways in Guobei coal mine, China. Young's modulus is an essential indicator to evaluate the deformation-resisting ability of coalcretes. In this study, for determining Young's modulus of coalcretes efficiently, an intelligent technique was proposed using the support vector machine (SVM) and beetle antennae search (BAS). The hyper-parameters of SVM were firstly tuned by BAS, and then the SVM-BAS model with optimum hyper-parameters was employed to model the non-linear relationship between the inputs (coal content, water content, cement content, and curing time) and output (Young's modulus). By combining these variables, 360 coalcrete samples in total were prepared and tested for establishing the dataset. The results show that BAS is more reliable and efficient than the trial–and–error tuning method. Moreover, by comparison with other baseline models such as back-propagation neural network (BPNN), logistic regression (LR) and multiple linear regression (MLR), the optimized SVM-BAS model is more reliable, accurate and less time consuming for predicting Young's modulus of coalcretes. Besides, by conducting sensitivity analysis (SA), the importance of different input variables was determined. This pioneering work provides guidelines for predicting Young's modulus of coalcretes and designing proper JG parameters in engineering applications.
KW - Beetle Antennae Search Algorithm
KW - Jet grouting coalcrete
KW - Prediction
KW - Support vector machine
KW - Young's modulus
UR - http://www.scopus.com/inward/record.url?scp=85062107145&partnerID=8YFLogxK
U2 - 10.1016/j.enggeo.2019.02.021
DO - 10.1016/j.enggeo.2019.02.021
M3 - Article
VL - 252
SP - 43
EP - 53
JO - Engineering Geology
JF - Engineering Geology
SN - 0013-7952
ER -