Tunnel face rock mass class rapid identification based on TBM cutterhead vibration monitoring and deep learning model

Abstract

Rapid identification of the rock mass condition at the tunnel face is a key problem for TBM operating parameters optimization and subsequent tunnel support measures selection. The vibration induced by the rock breaking contains essential information for evaluating the tunnel face condition. However, conventional vibration-based methods face difficulties in continuously obtaining vibration records for long tunnel sections. Additionally, there’s a lack of TBM cutterhead vibration monitoring, and they heavily depend on expertise and prior knowledge. In this study, an end-to-end deep learning (DL) method was developed for rock mass class identification of TBM tunnel working faces based on the measurement of TBM cutterhead vibration signals, including cutterhead vibration signal measurement, signal preprocessing, model training and optimization, and application verification. The model combines the advantages of 1DCNN, BiLSTM, and self-attention mechanisms, where the structural innovation of 1DCNN inspired by Inception v2 for multi-scale feature extraction. Which can automatically extract the spatial and temporal domain features in the signals to promptly identify the rock mass class at the working face without stopping the normal tunneling process. The accuracy on the test set is 95.89% compared to 85.34% for a traditional ML model, and it has better performance than other DL model architectures. The model underwent validation during subsequent TBM tunneling within the same project, successfully proving its practical reliability.

Introduction

Tunnel Boring Machines (TBMs) have been widely used to construct deep buried and long tunnels. Compared with the drilling and blasting method, TBM had the advantages of high efficiency and low labor intensity. However, TBM is more sensitive to geological condition^1,2. It is essential to adjust the operating and supporting parameters promptly in response to changes in the surrounding rock mass during TBM tunnellng³. Unknown geological hazards, such as faults, squeezing deformation, and rock bursts, may result in schedule delays and machine jamming, which can cause severe economic losses or even casualties⁴. Rock classification provides information that allows for operational improvement and an understanding of ground conditions for determining ground support requirements. Therefore, rapid, continuous, and precise rock mass condition identification is essential for the safety and efficiency of TBM tunneling.

The traditional geological survey in tunnel projects mainly depended on drilling exploration. However, the rock mass conditions along the tunnel alignment were not always reliable. Due to the narrow space between the cutterhead and the tunnel face, it is difficult to identify the tunnel face rock mass condition in time during tunneling. In fact, TBM tunneling is an interactive process between TBM and rock masses. The TBM operating parameters are closely linked to the surrounding rock mass conditions. Therefore, some data-driven methods were developed to identify the rock mass conditions based on TBM operating parameters^5,6. These methods almost adopted the same framework using different tunneling parameters and machine learning (ML) algorithms⁷. The typical framework for data-driven methods consists of five stages⁸: data acquisition, data preprocessing, selecting the input features, clustering and geology predictors. Although many studies have enhanced model performance using stacking ensemble learning or data enhancement methods. However, the accuracy of these models was generally around 84%~87%⁹. Where the precision of predictions for Grades II and V rock masses was imprecise, with accuracy rates ranging from 35–70%¹⁰. One of the main reasons was that the limited number of tunneling parameters was not directly related to the interaction process between the TBM and rock mass. Therefore, the instrumentation for diagnosing the physical behavior of the cutterhead and surrounding rock can enhance or independently achieve real time identification models for TBM tunnel face rock mass conditions. Several related studies, such as the stress-strain response analysis of disc cutters¹¹ and the interpretation of rock muck imagery¹², have been developed to collect direct information related to evaluate rock mass conditions in actual projects. However, the identification model of the tunnel face rock mass class based on the monitoring parameters has not been developed yet.

TBM vibration induced by the rock breaking contained essential information to evaluate the interaction between the cutterhead and the tunnel face rock mass conditions, and it was sensitive to the change of the geological condition^13–15. Walter¹⁶and Mooney and Walter¹⁷used accelerometers mounted on the bulkhead of earth pressure balance tunnel boring machine (EPB TBM) to monitor the bulkhead vibration. The bulkhead vibration was used to detect boulders in soft ground based on empirical observations of signal frequency images. Based on the vibration transmission principle proposed by Walter¹⁶, Liu et a¹⁸ obtained the vibration records during the tunneling at the bulkhead of EPB TBM. Converting the signals into time-frequency images, a signal classification model was established to identify the homogeneous soil ground, mixed face ground and separated rock block based on deep learning with an accuracy of 98.28%, and the cutterhead rotation period was recommended as the sample length. Fang et al.¹⁹ installed acceleration sensors near a slurry TBM’s cutterhead bearing and collected 45 min of vibration data across five strata. Using peak and RMS values of two time-domain features from 0.5-second signals and 11 driving parameters, they proposed ed an LSTM network model for stratum identification with 97.4% accuracy. This approach was still driven by tunneling parameters, as the extracted features were insufficient for characterizing non-stationary TBM vibration. S et al.²⁰ proposed a method for estimating the locations of the soil–rock interfaces based on vibration data during shield tunnelling. An equation was proposed to establish the relationship between the locations of the interface and the interval time of the vibration peaks corresponding to the disc cutters Yang et al.²¹ installed acceleration sensors at the back of the soil chamber wall and collected 300 sets of 20s signal data, Using the time domain and frequency domain characteristics of shield vibration signals under different surrounding rock UCS, the surrounding rock UCS identification model was established based on a BP neural network, It should be noted that this study did not account for the influence of joints in rock masses with varying UCS.

It could conclude that there are shortcomings in previous works on rock mass classification using vibration signals. In terms of vibration monitoring, it was difficult to continuously obtain vibration records for a long tunnel section. Moreover, the studies mentioned indicate a need for direct cutterhead vibration data collection, there was no vibration monitoring specifically based on the TBM cutterhead. Additionally, the conventional methods heavily rely on relevant expertise and prior knowledge for their performance. They require a lot of time for tedious extracted/selected feature engineering to achieve good performance. The feature set construction requires human involvement and relevant expertise. DL-based methods extracts features from time-frequency images converted from signals. When the signals are converted by complex signal processing methods, the features are altered, which intuitively suggests that the interpretability is weak, and the large dimensionality of visual data can also lengthen the network training process.

In deep learning algorithms, the most popular and well-known algorithms achieving high performance are CNN and RNN^22,23. With one dimension for processing time series data and two dimensions for processing images, 1DCNN was proven effective for implementing feature extraction from time series data collected by sensors²⁴. BiLSTM is a recent variant of RNN structures²⁵, which can fully capture the nonlinear trends, correlations, and temporal features of the time series data. Deep learning has gained extensive applications in the field of construction and tunnel engineering^26–28. 1DCNN and BiLSTM were adopted to predict the TBM penetration rate²⁹, attitude³⁰, energy consumption³¹, and to conduct the health status analysis of the cutter³², due to their capabilities of autonomous optimal feature extraction from the time series data. The above studies showed that from a large amount of input data, individual CNN and LSTM models can find nonlinear relationships between output and input variables. The combination of CNN modules with LSTM modules can achieve the advantages of each model to complement one another and further improve the performance.

In this study, the TBM cutterhead vibration accelerations in different rock mass classes were continuously obtained in each tunneling circle during TBM tunneling in a long tunnel section. Correspondingly, the rock mass parameters were collected during TBM tunneling. Additionally, an end-to-end DL model was proposed to automatically extract signal features and perform rock mass class identification by directly accepting the original vibration data input directly. Which combined multi-scale one dimensional CNN (1DCNN) module can extract spatial features from the acceleration data, and incorporated the BiLSTM with the self-attention mechanism. The main advantages and innovations of the model were that the 1DCNN module was inspired by GoogLeNet internal inception module, in which the 2DCNN structure was replaced by the 1DCNN structure. The model accepts raw cutterhead vibration signals, so the complexity of traditional feature extraction and conversion can be avoided. The model was verified using subsequently collected data in the same project. The influences of the signal segmentation length and model architecture on the prediction accuracy were analyzed.

Dataset preparation

Project overview

This study was conducted in a water delivery tunnel construction section XEIII in Xinjiang, China, which has a total length of 12.101 km, with a tunnel diameter of 7.83 m, and a longitudinal slope along the advance direction of 1/3000. The surface elevations range from 742 m to 877 m and a relative altitude of 135 m. Overburden of the whole section XEIII axis varies from 80 to 200 m.

According to the geological investigation report at the design stage, the geological profile of the tunnel alignment rock mass classes in the study area is presented in Fig. 1. The main lithologies along the tunnel alignment is granite, with a sub-blocky to blocky structure, characterized as hard and intact rock masses. The research section includes a fault f102 with the attitude of 275°SW∠ 60°m, the width of the fault zone is 3 m, mainly composed by the fractured rock, the angle between the fault strike and the tunnel axis is 43°. The geological report indicates that, except for the fault and its adjacent areas which consist of class IV and V rock masses, the remaining 99.5% is composed of class II, as shown in Fig. 1a.

Fig. 1.

Rock mass classes along the tunnel alignment (a. Investigation stage, b. Tunneling stage).

An open TBM, as shown in Fig. 2, was used for construction section XEIII. The excavation diameter of the TBM cutterhead is 7830 mm. The cutterhead was equipped with 46 single disc cutters and four center copy disc cutters. The design specifications of the open TBM are summarized in Table 1.

Fig. 2.

TBM employed in this study.

Table 1.

Technical parameters of TBM.

Technical parameters	Design value
Length/Weight of TBM	298 m/1500 T
Number/diameter of single disc cutter	46/483 mm
Number/diameter of center disc cutter	4/432 mm
Nominal cutter spacing	75 mm
Cutterhead diameter	7830 mm
Rated torque	1252 KN·m
Cutterhead rotating speed	0-4-7.6 r/min
Maximum thrust	27,488 KN
Maximum advance rate	120 mm/min

Rock mass classification during tunnel construction

The data used in this study were collected from chainage 61 + 400 to 62 + 985 m in section XEIII, which has a total length of 1585 m. During daily routine inspections and maintenance of the TBM, the rock mass condition of the tunnel face was recorded by entering the cutterhead. The rock mass condition of the tunnel wall was recorded by geological mapping during TBM tunneling. The recorded information includes tunnel mileage, rock type, rock mass structure and groundwater conditions, as well as the spacing, continuity, orientation and other conditions of each joint set. Rock cores from the tunnel wall were obtained by drilling holes at 50 m intervals. The uniaxial compressive strength tests were conducted to obtain the rock strength. The rock mass along the study section was classified based on HC (The National Standards Compilation Group of People’s Republic of China, 2008)³³. The tunneling stage rock mass classes distribution, as shown in Fig. 1b. The average geological parameters of the classes I to V are shown in in Table 2. The proportion off each rock mass class in the investigation and tunneling stage is shown in Fig. 3.

Table 2.

Geological average parameters of rock mass classes.

Class	Length /m	UCS/MPa	KV	Ground water	T
I	395	105	0.9333	Dripping	88.3
II	875	80	0.6348	Seepage	72.2
III	165	73	0.47	Seepage	63.6
IV	95	57	0.3476	Linear-flow	40.3
V	55	45	1.1378	Linear-flow	5.5

Fig. 3.

Percentage distributions of rock mass classes along the tunnel alignment (a. investigation stage, b. tunneling stage).

Installation of the cutterhead vibration monitoring system

A cutterhead vibration monitoring system used in dual-mode TBM³⁴ was installed on the TBM cutterhead. The system was composed of the data acquisition module, the communication and control module, and the analysis and display module. The data acquisition module collected the vibration signals of the cutterhead during the TBM tunneling. The communication and control module were utilized to connect the system software with the data acquisition module, enabling the transmission of control commands and monitoring data. Subsequently, these data were uploaded to the data analysis and display module installed in the TBM operation chamber. The sampling frequency must be at least twice the highest frequency of the signal to avoid aliasing, thus, it is set to 3200 Hz. The cutterhead vibration sensor as shown in Fig. 4. The three axes vibration sensor parameters are listed in Table 3.

Fig. 4.

TBM cutterhead vibration monitoring sensor.

Table 3.

The triaxial Cutterhead vibration sensor parameters.

Index		Value
Range		± 200 G
Precision		<=4 mg/LSB or < = 49 mg/LSB cutters
Broadband		0.05–1600 Hz, Default 1600 Hz
Sampling frequency		0–3200 Hz, Default 3200 Hz
Storage capacity		4 GBYTE + 512 KBYTE
Transmission rate		Max 2 Mbps
Impact resistance		10,000 G
Operating temperature		-40-85℃
Operating voltage		12 VDC
Thrust cylinder stroke		1850 mm

The data acquisition module was installed on the back of the cutterhead to measure the axial, radial, and tangential accelerations as shown in Fig. 5. The installation radius was 2.33 m. The cutter force in the tunnel axial direction was the largest. Additionally, the vibration in this direction was the most violent, which can better characterize changes in the rock mass condition of the tunnel face. Therefore, the vibration data in the direction of TBM tunneling was selected as the training and testing data.

Fig. 5.

The installation location of the vibration sensor.

The vibration data acquisition modes of the system included automatic and manual modes. The logic of the automatic acquisition mode was as follows: (1) automatic integral point time calibration with the sensor; (2) 5 minutes after time calibration, if the acceleration exceeded 2.34 G and lasted for more than 2 minutes, the system started to continuously acquire the vibration data for 120 seconds; (3) after the vibration data collection was completed, the system entered the next cycle. The manual mode was controlled by clicking the ‘Start Acquisition’ button, which initiated the data collection process.

In this project, the cutterhead averaged 6.65 RPM, advancing 8.78 mm per rotation and 11.6 cm over 120 s. Because the rock mass classification did not change abruptly along the tunnel alignment. Therefore, it was assumed that the rock mass class remained unchanged within a tunneling circle. That is to say, the obtained signals corresponding to a specific rock mass class can represent the entire cycle with a maximum 1.8 m. During TBM tunneling, vibration signals from Class I to IV rock masses were automatically collected. However, in Class V rock masses, TBM shut downs usually occurred. To enhance the quantity of the collected data, the manual mode was utilized for class V. Additionally, only the vibration data obtained during TBM tunneling was retained.

Cutterhead vibration signals dataset

In the studied section, a total of 860 vibration samples, each lasting 120 s with a sampling frequency of 3200 Hz were collected. The samples were labeled based on the rock mass class corresponding to the tunneling chainage at the time of vibration data collection. The typical raw vibration signals from rock mass classes I to V as show in Fig. 6.

Fig. 6.

Typical raw vibration signals for different rock mass classes.

Signal segmentation divided the original signal over a specific period into fragments of equal duration. For a 120 s vibration sample, the sliding window method was used to slice the vibration sequence, to weaken abnormal signal interference and ensure the integrity of the acceleration data edge information, meanwhile, the dataset was augmented.

To ensure the vibration signal represented for the rock mass class at the tunnel face, at least one period of the cutterhead rotation was used as the segmentation length. Given that in this study the lowest cutterhead rotation speed of the TBM in Class V was nearly 4 rpm, the segmentation length was initially set subjectively to 20 s to accommodate extreme conditions of low cutterhead rotation speed in tunneling. The original signal, lasting 120 s, was segmented into 10 signal segments with a duration of 20 s, with an overlap of 10 s between adjacent sliding windows. In summary, the whole dataset of the form (8600,64000), where 8600 is the number of samples contained in the dataset, 64,000 is the number of time step contained in each sample. The dataset composition is shown in Table 4.

Table 4.

Composition of the dataset.

Rock mass class	Tunnel length (m)	Number of signals (20s)	Signal duration (minutes)
I	395	1800	360
II	875	3000	600
III	165	2000	400
IV	95	1260	252
V	55	540	108

Proposed model architecture and training process

This study proposed an integrated model 1DCNN-BILSTM-SA that combines multi scale one dimensional CNN (1DCNN) and Bi-directional LSTM (BiLSTM) with self-attention (SA) mechanism for rock mass classification based on TBM cutterhead vibration signals, as shown in Fig. 7. The following briefly describes the methods used in the designed model. The training process for all models was implemented based on the Pytorch framework, Python 3.8 language environment.

Fig. 7.

The proposed model architecture.

1DCNN module

The proposed 1DCNN module structure was inspired by the module Inception V2 and some optimizations were made. In which the 2DCNN structure was replaced by a 1DCNN structure and configured with multi-pathway convolution. The most central part of GoogLeNet is its internal subnetwork module Inception V2³⁵, as shown in Fig. 8. The convolutional (conv) branch in the 1DCNN module focuses on extracting spatial information and learning complex patterns across channels, and the pooling branch specifically targets spatial information, emphasizing important features within the spatial domain. Since this study used a vibration signal dataset, each pathway was replaced by Conv1D and Maximum Pooling1D as the convolutional and pooling layers to handle time series data.

Fig. 8.

The structure of Inception V2 module.

This paper incorporates a batch normalization layer after all the convolutional layers and before the activation function. In terms of the activation functions, the model uses a combination of Leaky ReLU as the activation function. Although a large size convolution kernel can lead to a larger perceptual field, but generates more parameters. Instead, two consecutive 1 × 3 convolution layers can be replace a single 1 × 5 convolution, which reduces the number of parameters while maintaining the range of the field of perception. Finally, the multiscale features extracted by convolution kernels of different sizes and pooling layer were combined by concatenate at the end of each convolution branch and fed into the BiLSTM layer.

BiLSTM

The TBM cutterhead vibration signal is a typical time series data with obvious time dependence and continuity between the data. Therefore, it is necessary to focus on analyzing the collected temporal information. The BiLSTM contains two unidirectional LSTMs, each of which processes the input sequence in one direction (chronological and anti-chronological), and then merges their representations, the structure is shown in Fig. 9. The BiLSTM network can capture information that may be missed by a unidirectional LSTM network.

Fig. 9.

The BiLSTM structure.

Self-attention mechanism

The self-attention mechanism is introduced to disregard dependencies on external knowledge and effectively capture the crucial interrelation of features³⁶. By computing correlation scores between all positions in the input sequence, it generates a weight matrix and performs a weighted sum over the sequence, emphasizing critical information.

Each input , self-attention computes three vectors named query (), key () and value (). Then the query is matched with the keys to obtain the initial score according to Eq. (1).

1

where represents the dimension of . After passed through the SoftMax layer, the attention valuecan be calculated as a Eq. (2).

2

It is obvious that each contains the information of all inputs, so the self-attention mechanism can fully account for the diversity of the outputs of the model in the final aggregation process.

Class-balanced softmax cross-entropy loss

The number of vibration samples in different rock classes is often unbalanced, which can result in the model learning inadequately from samples of the minor class. The last layer of the model is the output layer containing the Class-Balanced SoftMax cross-entropy Loss³⁷. The core concept of this loss function is to assign a weight inversely proportional to the class sample size during cross-entropy loss calculation, amplifying minority class losses to encourage model focus. The introduction of a class-balanced loss function assigns greater weight to minority classes during the training process, thereby facilitating a better learning of features from these classes. Its calculation is given by Eq. (3).

3

Where , is the rock class label of a sample, is the number of training samples that are labelled with . The probabilities of all identification rock mass classes were assessed and the most probable class was output as the final result.

Proposed network architecture

The network architecture of the proposed model as shown in Fig. 9. It includes two 1DCNN layers and one BiLSTM layers with self-attention layer. The first layer of the network is an input layer, where the TBM cutterhead vibration signals measured by a sensor after segmentation were fed into the 1DCNN to extract multi-scale features, then the BiLSTM layer is used to learn the long-term temporal dependencies from the output of the 1DCNN. The “Concat” was used to connect the 1DCNN and BiLSTM layer. The self-attention layer was used to emphasize critical information and assigning them greater weights for rock mass class. Finally, using the Class-Balanced SoftMax Cross-Entropy Loss output the result of rock mass class. In addition, a dropout layer was added to reduce the interdependence between neurons. The discard ratio of the dropout layer is set to 0.2.

Model training and evaluation

The workflow for rock mass class prediction was mainly divided into three primary steps as shown in Fig. 10. The first step is data acquisition from the TBM cutterhead based on an accelerometer sensor. After signal segmentation, a total of 8600 samples were labeled by rock mass classes, with 75% of the dataset used for training and 25% for testing. The second step involved model building and training. The final step was model evaluation on the test set and subsequent TBM tunneling data within the same project.

Fig. 10.

Framework of the deep learning model for vibration-based rack mass class identification.

The model training process used the Adam optimizer to update the parameters of the network to reduce the value of the loss function. The exponential decay rates β1 and β2 of first-order moment estimation and second-order moment estimation were configured as 0.9 and 0.999 respectively. The initial learning rate was 0.001, and when the loss function value of the network experienced 10 epochs without decreasing, the learning rate was reduced to 0.1 times its previous value to promote convergence and reduce oscillation. Training was terminated if the loss function value did not decrease for 10 epochs. The batch size was set to 64. The specific details and parameters within the model, as shown in Table 5.

Table 5.

Details of the DL model structure and parameters applied.

Type	Filters	Kernel size/Stride	Units	Input size	Padding	Output size
Conv _branch1_1	25	1/2	-	(1,64000)	same	(25,32000)
Conv _branch2_1	10	1/1	-	(1,64000)	same	(10,64000)
Conv _branch2_2	25	3/2	-	(10,64000)	same	(25,32000)
pooling_branch3_1	-	3/2	-	(1,64000)	valid	(1,32000)
Conv _branch3_2	25	3/1	-	(1,32000)	same	(25,32000)
Conv _branch4_1	10	3/1	-	(1,64000)	same	(10,64000)
Conv _branch4_2	10	3/1	-	(1,64000)	same	(10,64000)
Conv _branch4_3	25	3/2	-	(25,64000)	same	(25,32000)
1DCNN layer 2		Same as Layer1
Concatenate	-	-	-		-	(100,16000)
BiLSTM	-	-	64	(100,16000)	-	(100,128)
Dropout	-	-	-	128	-	128
Self-attention	-	-	-	(100,128)	-	(128)
Fully connected	-	-	5	128	-	5

Figure 11 shows the model’s training process, the training loss is gradually decreasing while the accuracy increases gradually, and the model training experiences a small jitter and level off, proving the effectiveness of the training strategy.

Fig. 11.

Model training process.

The proposed model achieved an accuracy of 98.63% on the training set, which confirms the sensitivity of the proposed model to various rock mass classes. Usually, the model performs good in the training set but may not perform equally well in the test set, it may be overfitted. Therefore, the model’s performance should be evaluated primarily on the test set.

The evaluate metrics are calculated to assess the performance of the prediction results of rock mass classes, including Accuracy, Precision, Recall and F1-value. Their formulas are defined as follow:

5

6

7

8

Where the TN means the true negative amount, the TP means the true positive amount, the FN means the false negative amount and FP means the false positive amount.

Model performance

Model test results

The model performance in the test set was evaluated using the confusion matrix as shown in Fig. 12. The diagonal elements represented the rock mass class correctly identified by the model. The results indicated that Class I was recognized as Class II, Class V was recognized as Class IV with more errors. This misclassification primarily stems from ambiguities in the classification process. When adjacent rock mass classes exhibit geological similarity, operators tend to select similar tunneling parameters, which can lead to overlap of vibration signals at rock mass class boundaries, causing identification difficulties. In contrast, the identification performance for other rock mass classes was notably better.

Fig. 12.

Confusion matrix of the proposed model in the test set.

The accuracy, precision, recall, and F1-score for the test set, as show in Table 6. the accuracy of the model was 95.89% for the test set.

Table 6.

Rock mass class identification results in the test set.

Rock mass	accuracy	precision	F1-score	recall
I	0.9772	0.9816	0.9491	0.9433
II	0.9553	0.9590	0.9291	0.9357
III	0.9674	0.9771	0.9225	0.8987
IV	0.9214	0.9333	0.7119	0.6619
V	0.9725	0.7619	0.7297	0.8421

Results from different sample lengths

To evaluate the impact of signal length on identification accuracy of the proposed model. Five signal lengths of 1 s, 10 s, 15s, 20s, and 60 s were set to investigate their effects on the model’s accuracy, with all other model parameters remaining consistent. If the TBM cutterhead rotated at 6 revolutions per minute (RPM), the sample lengths corresponded to rotation angles of π/5, 2π, 3π, 4π and 12π, respectively. The test set results indicate a significant influence of sample length on model accuracy, as detailed in Table 7. Notably, cutterhead rotation per minute in various rock mass classes varied significantly. In the study section, the lowest cutterhead rotation speeds in Class IV and V were nearly 4.5 rpm and 3 rpm, respectively, and more than 6 rpm in the first three rock classes. When the segmentation duration is set to 15 s, the model achieves a peak testing accuracy of 97.56%, surpassing all other segmentation duration. The 15 s duration includes at least one period of the cutterhead rotation in the first four rock classes, and at least a cutter rotation of 270° in class V also provides sufficient rock information. Although the 60s duration contain sufficient cutterhead rotation cycles, the reduced number of samples leads to a decrease in model performance with an accuracy of 89.75%. Therefore, it is recommended to select the cutterhead rotation period as the sample length (set to 20 s in this study), which can achieve a relative balance between efficiency and accuracy.

Table 7.

Comparison of the results from different sample length.

Sample length(s)	Rotation angle	Points per sample	Number of samples	Accuracy on test set (%)
1	π/5	3200	188,340	85.43
10	2π	10 × 3200	18,060	95.61
15	3π	15 × 3200	11,180	97.56
20	4π	20 × 3200	8600	95.89
60	12π	60 × 3200	1720	89.75

Results from different DL model structures

In order to verify the better performance of the proposed 1DCNN-BiLSTM-SA model, a comparative analysis was conducted using several deep learning (DL) models with different structures. Including 1DCNN, BILSTM, 1DCNN-LSTM, and 1DCNN-BILSTM were compared under the same dataset. To ensure fairness and equality, all models adopted the same structure and parameter settings as the proposed model. Moreover, the model input signal duration (20s), computer configuration, program environment, and evaluation indicators for these comparisons were consistent. The comparison of accuracy chart between 1DCNN-BiLSTM-SA and other models is sown in Fig. 13.

Fig. 13.

Comparison of the results from different DL model structures.

The 1DCNN-BiLSTM-SA model demonstrated superior to other models in terms of accuracy. By comparing results with and without the Class-Balanced SoftMax approach, taking the imbalanced class IV and V rock masses as an example, the F1-scores increased from 0.7053 to 0.6980 to 0.7119 and 0.7297, respectively, the model accuracy improved by 1.33%. This indicates that the proposed mode can fully exploit the spatiotemporal correlation of the vibration signals, particularly outperforming single-structure models in rock mass classification. The 1DCNN-BiLSTM achieved an accuracy of 93.25%, demonstrating that the model of the self-attention mechanism has an effectively capture the crucial interrelation of signals features.

Comparing with ML method

As mentioned in the introduction, the performance of traditional ML methods relies heavily on extracted signal features. However, these handcrafted features, designed based on prior expertise, often introduce redundancy, increasing computational costs, reducing accuracy, and requiring significant time and labor.

The following time-domain features were extracted from 20 s segmented vibration signals: maximum, minimum, peak value, variance, standard deviation, root mean square, kurtosis, skewness, energy and average, the frequency domain features: frequency centroid (FC), mean square frequency (MSF), root mean square frequency (RMSF), variance frequency (VF), variance frequency deviation (VF) and kurtosis frequency (KUF) with skewness frequency (SKF). These features were input into the XGBoost ML model, with parameters optimized via grid search the learning_rate is 0.1, max_depth is 3, n_estimators is 200. The prediction accuracy of the XGBoost model with 85.34%.

Handcrafted features (e.g., time-frequency domain statistical measures) rely on prior knowledge for design and fail to adequately characterize the nonlinear and non-stationary characteristics of TBM vibration signals. Additionally, since the extracted features and classifiers are independent of each other, such models may introduce redundancy in feature extraction. In contrast, deep learning automatically extracts abstract features through multi-layer nonlinear transformations, adaptively capturing critical discriminative information. The results confirm that proposed model has a strong adaptive learning capability due to its automatic multi-scale feature extraction compared compared to manual feature engineering.

Model validation

After the 1DCNN-BiLSTM-SA model was proposed, its performance was verified during subsequent TBM tunneling within the same project. The rock mass classes in Sect. 63 + 105 to 63 + 145 m were obtained in a fault zone and its adjacent areas, which consist of class II (8 m), III (12 m), IV (13m) and V (7m) rock masses. The corresponding tunnel alignment distribution of rock mass classes and tunnel face rock mass, as shown in Fig. 14.

Fig. 14.

Tunnel validation section of rock mass classes distribution.

A total of 45 original vibration signals were automatically collected in this section. After signal segmentation (20 s), 450 times rock mass identifications were performed, achieving 95.11% accuracy and a 94.8% F1-Score. The confusion matrix of the model validation results, as shown in Fig. 15. Due to the imbalance in the training dataset of the model, the model may excel at predicting the majority class while lacking sufficient recognition capability for minority classes. As the project advances with further tunneling, the proposed model will be updated by expanding the dataset to enhance its performance in subsequent tunneling operations.

Fig. 15.

Confusion matrix of validation results.

Conclusion

To address the challenges of obtaining cutterhead vibration signals in long tunnel sections and the heavy reliance on expertise for vibration-based rock mass identification. A vibration monitoring system was installed on the TBM cutterhead to obtain vibration signals during each tunneling cycle for a long tunnel section. An end to end deep learning (DL) model for rock mass class identification was proposed based on vibration signals.

The model innovatively integrates a 1DCNN (inspired by Inception v2) for multi-scale feature extraction, utilizes BiLSTM to capture sequential signal information, and employs a self-attention mechanism to adaptively assign weights to different rock mass classes. Additionally, the class-balanced Softmax activation function was used to mitigate data imbalance effects, thereby bypassing the complexity of traditional feature extraction and conversion.

Analysis of signal segmentation length showed when the input sample length was set close to the cutterhead rotation period, the model achieved the highest accuracy 97.56%. Further comparison verified that the accuracy the proposed model outperforms other DL structures and traditional ML model with statistical hand-designed features.

Finally, the effectiveness of the proposed model was verified using the subsequent data in the same project. It enables real-time ground condition detection at the tunnel face without interrupting operations, allowing timely TBM parameter adjustments. Future work will enhance performance by expanding the dataset and integrating operational parameters. We plan to rigorously test the model across diverse geological environments using data from multiple projects, ensuring robustness and applicability in varied construction environments.

Author contributions

Q.T. conceptualized and developed the method, planned and supervised the study, first draft of the manuscript writing and preparation. Q.T., Q.H., G.W. and X.L., collected and analyzed the data, assisted with the model’s analysis and results. P.H., assisted with the literature survey and editing.

Data availability

Data supporting the findings of this manuscript are available from the corresponding authors upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.