Real-Time Aarly Warning of Clogging Risk in Slurry Shield Tunneling: a Self-Updating Machine Learning Approach
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37th International Symposium on Automation and Robotics in Construction (ISARC 2020) Real-time Aarly Warning of Clogging Risk in Slurry Shield Tunneling: A Self-updating Machine Learning Approach Qiang Wanga, Xiongyao Xie a, and Yu Huang a aDepartment of geotechnical engineering, Tongji University E-mail: [email protected], [email protected], [email protected] Abstract – pressure balanced (SPB) tunnel boring machine [1,2]. Clogging is one of the main risks when slurry The clogging problem will trigger risks during tunneling shield tunneling in the mixed ground condition construction, such as slower tunneling efficiency[3], containing clayey soils. Severe consequences, such as instability of tunnel face[4], higher wear of cutterhead[5], instability in the excavation face and high cutter wear, etc. As a typical kind of clayey soil, mudstone has a high may occur if the shield machine operators don’t take potential to result in a clogging problem[4]. Several specific measures to eliminate clogging. Therefore, projects in China have been encountered with clogging early warning of clogging during one ring excavation problems in mudstone rich area, for example, Wuhan becomes essential for the safetyf o tunneling. The Sanyang cross-river road tunnel[6], the metro tunnel line currently available methods to judge the clogging 1&2 in Nanning city[7], Nanchang metro line 1[8], risks focus mainly on field engineer experience, which Nanjing Yangze river tunnel[9]. The filed experience seems arbitrary sometimes. In this paper, an indicates that it’s difficult to maintain the normal automatic self-updating machine learning approach tunneling state in mudstone rich area, especially for the is proposed to realize the real-time early warning of mixed ground containing mudstone. clogging. More specifically, the random forest is Clogging is induced by the stickiness of the excavated employed with several minutes (e.g. 2 min at the clayey soil, which can be influenced both by the clay beginning of one ring excavation) of tunneling mineralogy and the slurry flow behavior [2,10]. There are parameters as input. When one ring has been finished, several laboratory tests have been presented for it will become a new training sampleo t update the evaluating the clogging potential of one certain kind of model via randomized parameter optimization. With soil and slurry flow behavior [2,10–12], which brings out the case study of Nanning metro line 1, it’s found that some directly or indirectly methodologies for clogging the self-updating mechanism is beneficial for better judgment. These clogging elevation methods rely on the judgment of clogging, and 4 minutes tunneling soil properties and most of them focused on either sand parameters (24 samples) are suitable for early or clayey soils, which has a limitation in mixed ground warning. The model can achieve an accuracy of 95% conditions[2,13]. Moreover, there is still a lack of early in the mixed ground condition. Meanwhile, in warning methods for clogging during the tunneling comparison with the other machine learning process. approaches is also discussed. With the training data The criteria to evaluate clogging potential mainly set updating mechanism, the RF model can use less focused on the soil properties but pay little attention to tunneling datao t realized the clogging prediction. the slurry or foam properties well as shield driver According to the feature importance result, the operations in shield tunneling process[14]. When SPB variation of cutterhead torque is essential for clogging shield tunneling in the mixed ground containing prediction. mudstone, it is difficult to determine whether clogging occurs only rely on the geological conditions. Therefore Keywords – it is crucial to develop an early warning approach for Shield tunneling; Clogging; Early warning; clogging both based on the geological conditions and Random forest; Self-updating tunneling parameters. The data-driven approach, such as the random forest (RF) method, seems appropriate in the tunneling process. The clogging state can be regarded as 1 Introduction an abnormal tunneling situation, thus it can be considered Shield tunneling in clayey soils is frequently as a binary classification problem. RF algorithm for the obstructed by clogging problems, both for earth pressure development of descriptive and predictive data-mining balanced (EPB) tunnel boring machine (TBM) and slurry models has become widely accepted in engineering 600 37th International Symposium on Automation and Robotics in Construction (ISARC 2020) applications, promising powerful new tools for practicing i-1 rings finished L-i+1 rings Suppose L rings in total, and #i engineers [15]. now tunneling at ring #i Kohestani et al.[16] presented an RF-based model for N=4 N=3 prediction of seismic liquefaction potential of soil based N minutes tunneling data are N=2 N=1 selected for early warning 160 on the cone penetration test data, and the proposed RF 60 (N=0.5 ,1 ,2 ,3…) -40 models provide more accurate results than the artificial 0 1 2 3 4 5 6 Time(min) neural network (ANN) and the support vector machine Training set: i-1 rings data with (SVM) models. Zhou et al.[17] employed eleven N minutes of tunneling data as algorithms to predict the rockburst classification and input feature, i-1 rings clogging found the RF achieved the best result. The above- state as output i-1 rings training data set mentioned applications of the RF-based classification model are all static models. They employed well-trained Clogging prediction model i via training samples 5-fold cross validation and … validation samples RF models for all test data sets, which is not suitable for randomized search the shield tunneling process. As a real-time early warning model for clogging, the proposed model should be Predict clogging state of ring #i #i trained via as few rings as possible and is supposed to i=i+1 using model i realize self-updating as new rings have been finished. As a result, a training data set updating mechnism will be When ring #i has finished, determine the clogging state via #i designed in this paper to realize the real-time early three criteria warning of clogging risk in SPB shield tunneling. i rings finished L-i rings The remainder of the paper is organized as follows: Training set update by adding In section 2, we introduce the real-time early warning the data of ring #i model based on RF. In section 3, the case study in i rings training data set N Nanning metro will be presented. The impact of training i=L? data set updating mechanism will be discussed in section 4 before presenting the conclusions. Y End 2 Real-time early warning of clogging Figure 1. Flow chart for real-time early warning of risks based on RF clogging with training data updating Figure 1 shows the process for real-time early Random forest (RF) is an ensemble learning method warning of clogging risks with training data updating for classification that operates by constructing a large during shield tunneling construction. Suppose there are L number of decision trees at training time and outputting rings in total for a tunnel section, and the shield machine the class that is the mode of the classes of the individual is going to tunneling at the ring #i ( iL ). Firstly, we trees[19]. As illustrated in Figure 2, samples and features select N minutes tunneling data at the beginning of each are randomly selected from the data set using the i-1 rings as the early warning input feature bootstrap aggregating method. Therefore, many sub- ( N = 0.5,1,2,3 ) and take the i-1 rings clogging state as samples are created by choosing random features with replacement. Then each decision tree will be trained on output, which will be composed as the training data set. the sub-sample and the final class (clogging or normal) Secondly, the ith clogging prediction model is trained will be determined by averaging the probabilistic with the above training data set, whose hyper-parameters prediction of all the trees. Several hyper-parameters in are determined by the 5-fold cross-validation and the RF will be determined by cross-validation, such as randomized search[18]. Then, the proposed prediction the number of trees in the forest ( n_ estimators ), the model is used to predict the clogging state of ring #i via the N minutes tunneling data at the beginning of the ith maximum depth of the tree ( max_ depth ), etc. To ring. Finally, when the ring #i has been finished, we will improve the training efficiency during shield tunneling determine the real clogging state for the ith ring based on construction, the randomized search strategy is employed the above mentioned three criteria. Moreover, the instead of the traditional grid search method. This training data set will be updated by adding the input strategy implements a randomized search over the hyper- feature and clogging state of ring #i. This process will parameters, where each setting is sampled from a continue until the tunneling section has been finished. distribution over possible hyper-parameter values. 601 37th International Symposium on Automation and Robotics in Construction (ISARC 2020) Data set shown in Figure 3 (a), the BR section was excavated with a Herrenknecht SPB shield machine with a diameter of Sub-sample 1 Sub-sample 2 Sub-sample j Sub-sample n 6.28 m. The tunnel in the BR section is surrounded by …… complex geological and hydrological conditions. The tunnel passing through geological profiles (illustrated in Figure 3 (b)) are mainly round gravel, mudstone, and …… sand, which are shown in different colors in Figure 3 (b). The mixed ground containing mudstone locates around ring # 120 to ring # 220, and ring # 283 to ring # 470.