Abstract
By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method.
Highlights
A surrounding rock loose zone refers to the stress redistribution of surrounding rock after excavation and the stress variation in surrounding rock, which leads to stress concentration
To compare the model’s generalization capability, three measurements are introduced to test the capability of model fitting data and prediction [62], as follows: (1) Root mean square error (RMSE)
According to the calculation results, the According to the results of six cross validations, the optimal value of the model core parameters is kernel parameters of Gauss-RVM are 12.5, and those of the Mexican relevance vector machine, the chosen when the root mean square error is the smallest
Summary
A surrounding rock loose zone refers to the stress redistribution of surrounding rock after excavation and the stress variation in surrounding rock, which leads to stress concentration. With the development of structural health monitoring [17,18,19], the methods to determine the loose zone thickness of roadway surrounding rock generally include acoustic wave test, numerical simulation and, support vector machine prediction [20,21]. The support vector machine prediction method [41] can solve the problems of nonlinearity and small samples, and has achieved some success in the prediction of roadway surrounding rock loose zone. This paper has combined wavelet technology with RVM, namely wavelet relevant vector machine (WRVM), and applied it to prediction of surrounding rock loosening zone of roadway.
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