Abstract
In the construction of rock tunnels, the penetration rate of the tunnel boring machine (TBM) is influenced by many factors (e.g., geomechanical parameters), some of which are highly uncertain. It is difficult to establish a precise model for predicting the penetration rate on the basis of the influencing factors. Thus, this work proposed a useful method, based on the relevance vector machine (RVM) and particle swarm optimization (PSO), for the prediction of the TBM penetration rate. In this method, the RVM played a vital role in establishing a nonlinear mapping relationship between the penetration rate and its influencing factors through training-related samples. Then, the penetration rate could be predicted using some collected data of the influencing factors. As for the PSO, it helped to find the optimum value of a key parameter (called the basis function width) that was needed in the RVM model. Subsequently, the validity of the proposed RVM-PSO method was checked with the data monitored from a rock tunnel. The results showed that the RVM-PSO method could estimate the penetration rate of the TBM, and it proved superior to the back-propagation artificial neural network, the least-squares support vector machine, and the conventional RVM methods, in terms of the prediction performance. Moreover, the proposed RVM-PSO method could be applied to identify the difference in the importance of the various factors affecting the TBM penetration rate prediction for a tunnel.
Highlights
As a consequence of sustained interest in the development of underground space, tunnels play a more and more important role in human production activities and life
Accurate prediction of the tunnel boring machine (TBM) penetration rate is of great significance to tunnel constructions, for purposes of optimizing design schemes, saving economic costs, and evaluating the feasibility of engineering constructions. us, a useful intelligent method, relevance vector machine (RVM)-particle swarm optimization (PSO), is developed to estimate the penetration rate of the TBM
En, the prediction performance of the RVM-PSO method was compared with that of the BP-artificial neural network (ANN), LS-support vector machine (SVM), and conventional RVM methods. e main conclusions are drawn as follows: (1) e penetration rate of the TBM is affected by various factors, and there are complex nonlinear relationships between the penetration rate and its influencing factors. e RVM-PSO method developed in this study can effectively establish a nonlinear correlation between the penetration rate and its main influencing factors and can achieve a relatively good prediction of the TBM penetration rate
Summary
As a consequence of sustained interest in the development of underground space, tunnels play a more and more important role in human production activities and life. For the penetration rate prediction, it is, necessary to explore a high-efficiency and accurate-enough model that can solve the parameter determination problem and obtain better prediction results To address this issue, a useful method is developed in this study for predicting the TBM penetration rate by integrating the PSO optimization algorithm into the relevance vector machine (RVM) method. Since the RVM contributes to establishing a nonlinear mapping relationship between the output/target and input vectors and PSO helps significantly to optimize the parameter (i.e., the basis function width) of the RVM, the RVM-PSO method is developed by integrating PSO into the RVM. E RVM model should be optimized automatically by updating the particles in PSO to reduce the error between the predicted results and the expected results until the prediction corresponding to a certain hyperparameter meets the accuracy requirement.
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