1. Introduction
Tunnel boring machines (TBM) are widely applied in various tunnel construction projects, such as subways, mining ores, railways, etc., due to advantages of higher reliability, safety, and environmental friendliness [
1].
Figure 1 illustrates a typical structure of the TBM, which contains multiple sub-systems, such as the cutterhead driving system, thrust system, cutterhead system, etc. In real-world applications, TBMs generally work in heterogeneous and complicated geological environments, such as spalling, faulting, fracturing, rock bursting, squeezing, swelling, and high water in the flow [
2], that pose severe challenges to the operation of TBMs. A schematic illustration of the geological conditions of a tunnel is demonstrated in
Figure 2. To ensure construction safety and reduce energy consumption, it is desirable to accurately predict the dynamic load (generally referring to the cutterhead torque) under spatio-temporally varying geological conditions and to dynamically adjust the TBM control parameters during excavation.
In general, the prediction methods for cutterhead torque can be roughly grouped into three types: rock–soil mechanics methods, empirical methods (combined with experiments), and soft computing methods. The rock–soil mechanics method establishes a model according to the force balance among rock, cutters, and internal machinery [
4,
5]. The empirical models are based on engineering experience involving a large amount of laboratory tests, field measurements, and construction records [
6,
7]. The soft computing methods are developed as data-based solutions for predicting the TBM’s load through mathematical mapping. Rostami [
8] elaborated theoretical and empirical methods in a recent review. S. K. Shreyas [
9] and Shahrour Isam [
10] provided a brief retrospect of recent application of soft computing methods to predict various parameters in tunneling and underground excavations.
By dividing the tunnel alignment into three general sections in terms of geological and geotechnical conditions, Avunduk et al. [
12] proposed an empirical model for predicting excavation performance of TBM. Through the mechanical decoupling method for analyzing the cutterhead–ground interaction, Zhang et al. [
13] proposed an approximate calculation method for determining the load acting on the cutterhead. Based on the interaction between the TBM and excavated material, Faramarzi et al. [
14] applied the discrete element method (DEM) to evaluate the TBM torque and thrust. Rock–soil mechanics methods and empirical models are both based on the premise that the geological information is known. However, the accurate prediction of a geological profile before excavation is a hard and challenging task. In tunneling and underground excavation, the geological information is obtained through borehole sampling, and the stratum between sampling points are usually estimated by linear fitting. The distance between the sampling points is typically considerable, and the relevant result is often different from the real distribution, which may affect the accuracy of the rock-soil mechanics methods and the empirical models [
15].
Assisted by the advancement of sensor and measurement technology, modern TBMs can record series of operation parameters closely related to dynamic load, which provides a basis for the practical application of soft computing methods. Sun et al. [
16] utilized the random forest (RF) algorithm to design a predictor for TBM load. Kong et al. [
17] took geological conditions and operational data as inputs to build a prediction model based on the RF for predicting driving forces of a TBM in a soil–rock, mixed-face ground. Li et al. [
18] used the one-dimensional convolutional neural networks and long short-term memory network (CNN-LSTM) to predict cutterhead speed and penetration rate (PR). Qin et al. [
19] applied a deep neural network-based method to predict dynamic cutterhead torque based on operating data and status parameters. Suwansawat et al. [
20] applied the multi-layer perceptron (MLP) to determine the correlation among TBM operational data, groundmass characteristics, and surface movements. Lau et al. [
21] used a radial basis function (RBF) to estimate tunneling production rates of successive cycles. Gao et al. [
22] used three kinds of recurrent neural networks (RNNs) to deal with TBM operating parameters’ real-time prediction. Soft calculation methods usually involve the optimization of many parameters, and the selection of parameters based on experience will reduce the accuracy of the analysis results. To deal with this problem, there have been many hybrid methods proposed in the literature. For example, Zhou et al. [
23] applied three optimization algorithms to optima of the hyper-parameters of the support vector machine (SVM) technique in forecasting the advance rate (AR) of TBMs. Armaghani et al. [
24,
25] proposed two hybrid, intelligent systems, namely the particle swarm optimization (PSO)-artificial neural network (ANN) and the imperialism competitive algorithm (ICA)-ANN, to approximate the PR and AR of TBMs, respectively.
Although relatively accurate prediction results can be achieved by soft computing approaches, most of them generally assume that training samples and future test samples have identical distribution characteristics, and their practicability still has room for improvement. During the excavation process, TBMs encounter varying geological and working conditions, such as accelerating, turning, jamming releasing, etc., resulting in considerable changes in the underlying pattern of operation data over space and time. So, historical datasets behave as a non-stationary time series that makes the correlation among parameters in a high degree of complicated, changeable, and challenging conditions to be described by simple or fixed mathematical expressions. Hence, it is a serious challenge to extract common knowledge from historical datasets to assist in building an adaptive model which dynamically changes with geological conditions and operating parameters, for implementing dynamic cutterhead torque prediction at the current moment. To a certain degree, this problem is similar to the paradigm of transfer learning [
26,
27], which addresses this problem by utilizing experiences gained from source tasks to improve the learning of new related tasks. Hu et al. [
28] applied the concept of transfer learning for efficient wind speed prediction. The prediction model was trained on samples from older data-rich farms to extract wind speed patterns, and then finely tuned with samples from newly built farms. Rui et al. [
29] constructed a novel transfer learning paradigm for time series prediction, and the principle of transfer learning is employed. However, TBM’s historical data contains a variety of geological information and working modes. So, directly adopting the most intuitive transfer learning method without distinguishing all the working modes in the historical data may result in negative transfer problems.
Herein, a novel hybrid data-mining framework based on clustering, multitask learning (MTL), transfer learning, and least-squares support vector regression machines (LS-SVR), abbreviated as TRLS-SVR, is proposed for dynamic cutterhead torque forecasting of TBMs. In this framework, LS-SVR is selected as a baseline model, which has a powerful capability to capture underlying nonlinear relationships for a complex system. The underlying patterns in historical data are effectively divided according to the relationship among attributes [
30]. To take advantage of the knowledge contained in different working modes and to eliminate the damage from dataset bias, we adopt the idea of MTL [
31], which explicitly exploits commonalities and differences across multiple working modes by learning them simultaneously rather than individually, to improve knowledge extraction ability. Based on the common knowledge extracted from historical data, we utilize the newly collected operation data to continuously update the pattern-specific biases parameters for adapting to the changing geological and working conditions. This study offers the following innovations and contributions. (1) The unsupervised learning algorithm for data clustering is combined with the MTL paradigm to explore and exploit the correlations among multiple working modes by learning simultaneously rather than individually, which enhances the ability of extracting public knowledge from a diversely recorded TBM historical dataset. (2) It employs a transfer learning paradigm to reuse the public knowledge that is contained in the historical dataset to supply new data, and it alleviates random noise interference and fits the varying geological and working conditions well. (3) The TRLS-SVR performs superior performance in geologically complex and changeable locations, compared with that of conventional data-driven algorithms.
The rest of this study is organized as follows.
Section 2 presents details of the proposed framework. In
Section 3, the experimental verification is presented. In
Section 4, some discussions on experimental results are provided.
Section 5 concludes the whole study and provides future work.
5. Conclusions
In this study, a novel hybrid transfer learning framework named TRLS-SVR, that aims to enhance the accuracy of TBM dynamic cutterhead torque prediction, is proposed. In the proposed framework, the underlying patterns in historical datasets were effectively divided according to the relationship among attributes. The idea of MTL was adopted to exploit commonalities and differences across various working modes by learning them simultaneously rather than individually, to capture the public knowledge from historical datasets. In order to cope with the changing geological and working conditions, the idea of transfer learning was adopted and the newly collected operation data were utilized to continuously update the parameters of the forecasting model as a supplement. Real-world, in situ operational and status parameters from a tunnel located in Shenzhen, China, were utilized to evaluate the efficacy and superiority of the proposed framework. Experimental results demonstrated that the TRLS-SVR alleviated the shortcoming of traditional statistical data-driven methods, which can only predict the average value and changing trend of the cutterhead torque but cannot achieve dynamically and accurately the prediction of the load. Additionally, compared with the method of an online learning paradigm, which puts more attention to data closer to the excavation point, the framework has stronger robustness. This is because the model can use the knowledge contained in historical data to reduce the impact of random noise and alleviate over-fitting issues. In summary, the major novelty of this study is to provide a first test of merging MTL and transfer learning for TBM dynamic cutterhead torque prediction. Though the framework is presented in the context of dynamic cutterhead torque prediction of TBM, it can be easily extended to the status monitoring of other engineering systems, such as wind power equipment, automobiles, etc. In the near future, we plan to further investigate the adaptable adjustment of TBM’s operating status based on the proposed framework, which is of great significance to the operation safety and energy consumption.