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Evaluation of earthquake-induced liquefaction potential is crucial in the design phase of construction projects. Although several machine learning models achieve good prediction accuracy on their particular datasets, they may not perform well in other liquefaction datasets. To address this issue, we proposed a novel hybrid classifier ensemble to improve generalizability by combining the predictions of seven base classifiers using the weighted voting method. The applied base classifiers include back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and naïve Bayes. The hyperparameters and weights of the base classifiers were tuned using the genetic algorithm. To verify the robustness of the classifier ensemble, its performance was tested on three datasets collected from previous published researches. The results show that the proposed classifier ensemble outperforms the base classifiers in terms of a variety of performance metrics including accuracy, Kappa, precision, recall, F1 score, AUC and ROC on the three datasets. In addition, the importance of influencing variables was achieved by the classifier ensemble on the three datasets to facilitate the future data collecting work. This robust ensemble method can be extended to solve other classification problems in civil engineering. [ABSTRACT FROM AUTHOR]

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. Highlights • The jet grouting technique was firstly utilized in underground coal mine for generating coalcrete to reinforce the roadway stability • A total of 360 specimens were tested to determine the deformation-resisting ability of coalcrete. • A support vector machine model was proposed for predicting Young's modulus of coalcrete. • The hyperparameters of support vector machine were tuned by beetle antennae search algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]