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TBM performance prediction with Bayesian optimization and automated machine learning
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DOI：10.1016/j.tust.2020.103493.png)
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Accurately predicting the performance of a tunnel boring machine (TBM) is important to safe and efficient tunneling. The application of machine learning algorithms to TBM performance prediction creates several challenges. Such prediction is a nontrivial task involving procedures such as data preprocessing, selection of a machine learning algorithm and optimization of the related hyperparameters. The demand for expert knowledge has restricted the application of machine learning methods to TBM performance prediction, and it is meaningful to study predicting TBM performance automatically. In this paper, we explore three approaches to TBM performance prediction using Bayesian optimization and automated machine learning (AutoML). In the first study, Bayesian optimization is used to determine the optimal hyperparameters of various machine learning algorithms, including support vector regression (SVR), decision tree, bagging tree, random forest and AdaBoost. We attain the minimum mean squared error (MSE) values of 3.135 x 10(-2) and 3.177 x 10(-2) for a decision tree and SVR, respectively. In the second approach called the neural architecture search (NAS), the optimal combination of architecture, hyperparameters and the training procedure of an artificial neural network is found in a single operation. We obtain the optimal results of 3.514 x 10(-2) and 3.237 x 10(-2) if complete and simplified NAS are used, respectively. In the third method, the best combination of a data preprocessing method, a machine learning model and the related hyperparameters is found, and an optimal MSE value of 3.148 x 10(-2) is obtained using AutoML. In all three studies, we obtain state-of-the-art prediction results that are superior to a previous best prediction result of 3.500 x 10(-2). The prediction results prove that Bayesian optimization and AutoML are powerful tools that can not only effectively predict TBM performance but also reduce the demand for expert knowledge of machine learning.
Keyword:
Performance prediction
Bayesian optimization
Machine learning
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