Abstract 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 × 10 - 2 and 3.177 × 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 × 10 - 2 and 3.237 × 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 × 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 × 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.

中文翻译：

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使用贝叶斯优化和自动机器学习进行 TBM 性能预测

摘要 准确预测隧道掘进机（TBM）的性能对于安全高效掘进具有重要意义。机器学习算法在 TBM 性能预测中的应用带来了一些挑战。这种预测是一项重要的任务，涉及数据预处理、机器学习算法的选择和相关超参数的优化等过程。对专家知识的需求限制了机器学习方法在TBM性能预测中的应用，研究TBM性能自动预测具有重要意义。在本文中，我们探索了使用贝叶斯优化和自动机器学习 (AutoML) 进行 TBM 性能预测的三种方法。在第一项研究中，贝叶斯优化用于确定各种机器学习算法的最佳超参数，包括支持向量回归 (SVR)、决策树、装袋树、随机森林和 AdaBoost。对于决策树和 SVR，我们分别获得了 3.135 × 10 - 2 和 3.177 × 10 - 2 的最小均方误差 (MSE) 值。在称为神经架构搜索 (NAS) 的第二种方法中，可以在单个操作中找到架构、超参数和人工神经网络训练过程的最佳组合。如果使用完整和简化的 NAS，我们分别获得 3.514 × 10 - 2 和 3.237 × 10 - 2 的最佳结果。在第三种方法中，找到了数据预处理方法、机器学习模型和相关超参数的最佳组合，并且最佳 MSE 值为 3。148 × 10 - 2 是使用 AutoML 获得的。在所有三项研究中，我们都获得了优于先前最佳预测结果 3.500 × 10 - 2 的最新预测结果。预测结果证明，贝叶斯优化和AutoML是强大的工具，不仅可以有效预测TBM性能，还可以降低对机器学习专家知识的需求。