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

By Yuantian Sun, Junfei Zhang, Guichen Li, Yuhang Wang, Junbo Sun, Chao Jiang. International journal for numerical and analytical methods in geomechanics (Print), 2019

146 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9beb884586d41bdfbea9ed19c04423599ee8be01

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In Civil and Structural Engineering

Citations in the corpus

Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model (2019, methodology): “BAS algorithm is proposed recently, which can be efficiently used for optimizing the global problems and has been used for selecting the optimum parameters of algorithms such as BPNN and SVM [33, 34].”