Search

This paper establishes a digital twin (DT) enabled model that aims to achieve real-time monitoring and control to enhance the overall performance of tunnel boring machines (TBM) during excavation. Through the Internet of Things (IoT) sensors that are installed on TBM, the actual operating status is transferred from the physical object to the digital model. Deep learning models that integrated graph convolutional network (GCN) and long short-term memory (LSTM) networks are employed for reliable TBM performance estimation. An online multi-objective optimization (MOO) approach is then proposed to determine the optimal operating parameters for advanced control. Real data from Singapore's Circle Line 6 C885 project is utilized as a case study to examine the applicability of the developed digital twin model. TBM's penetration rate, over-excavation ratio, energy consumption, and tool wear are selected as the objectives (O 1 to O 4) for optimization. The results indicate that (1) The digital twin model can provide a reliable estimation of TBM's performance indicators with a high coefficient of determination (R 2) values. (2) TBM's overall performance can be effectively improved through the proposed online optimization method, with an overall improvement of 21.12%. (3) The proposed online optimization method is more practical and creates better improvement than the conventional MOO approach. This study addresses the existing research gaps of lacking a reliable method to guide TBM operation with enhanced performances for the major objectives. It also contributes to proposing a novel intelligent approach that integrates DT, deep learning, and an online-NSGA-II method for TBM operation optimization during tunnel excavation. • A digital twin enabled approach is proposed for real-time intelligent control for TBM operation. • An MOO approach that integrates deep-learning and online optimization is established. • Data from a realistic tunnel project in Singapore is used to demonstrate as a case study. • TBM's efficiency, safety, energy consumption and tool wear are considered as objectives. • An overall improvement of 21.12% is achieved by implementing the proposed method. [ABSTRACT FROM AUTHOR]

The traditional mode of Tunnel Boring Machine (TBM) operation is limited in their applicability and efficiency to meet the growing demand for underground spaces. Current methods to address this problem often lack physics interpretability and automatic adaptivity. In response to this issue, this paper describes an approach that integrates a physics-informed reinforcement learning (PIRL) algorithm into a TBM operation. The method combines a physics-informed machine learning (PIML) model and physics reward functions considering the working mechanism of earth pressure balance (EPB) TBMs, forming into a physics-informed Twin Delayed Deep Deterministic (pTD3) algorithm. The study reveals that the TBM performance can be improved by 69.3% using the pTD3 algorithm compared to manual operation. Integrating physics knowledge into reinforcement learning proves significantly effective in enhancing the TBM operations. The proposed method has the potential to revolutionize TBM operation and pave the way for more efficient, reliable, and automatic tunnel construction. • A pTD3 algorithm is developed to enhance the performance of EPB TBMs. • The algorithm considers the advance speed and earth pressure balance as design targets. • A PDNN model is trained to simulate the excavation process of the EPB TBM. • Physics laws are integrated into the reward function and constraints. • pTD3 is found to significantly enhance the performance of the TBM. [ABSTRACT FROM AUTHOR]

In order to avoid safety accidents caused by the unbalance of earth pressure in the chamber during the tunneling process of shield tunneling machine, it is very crucial to make a scientific and accurate dynamic prediction of earth pressure change. Therefore, a hybrid deep learning model is built by using discrete wavelet transform (DWT), one-dimensional convolution neural network (1DCNN) and long-short term memory (LSTM), implementing the scientific prediction of multipoint earth pressure variation trend in this paper. The DWT is introduced to reduce noise in training data, reduce training cost, and improve training accuracy; the 1DCNN is employed to extract features quickly and reduce the training time of LSTM; in order to further improve the prediction performance of the model, the Ranger optimizer is used to optimize the LSTM, which not only improves the prediction accuracy, but also makes the training process of the prediction model more stable. Finally, the validity of the model is verified based on the actual construction data. The results show that the overall and local earth pressure variation trends in the sealed chamber are clearly visible, which can provide decision-making basis for the shield machine to realize automatic, intelligent and safe excavation construction. • The hybrid deep learning is used to construct the prediction of earth pressure. • Range optimizer is used in LSTM to further improve the prediction performance. • The model in this paper has strong practicability and high prediction accuracy. • The prediction method of earth pressure variation trend is proposed. [ABSTRACT FROM AUTHOR]

Shield tunneling has been prevalent in tunnel construction since its introduction into the field. To take advantage of the massive data generated during tunneling and to assist in engineers' judgement, deep learning models have been widely applied. A comprehensive survey is presented in this paper to organize emerging research and propose future directions. Typical types of data in shield and the corresponding pre-processing approaches are summarized and listed. Specific application scenarios are defined, including the recognition, predication, and control of external environments, shield efficiency, and shield safety. The explainability and generalizability of the applied deep learning models are also analyzed to evaluate their performance. The research challenges are proposed, including the lack of high-quality datasets, limited evaluation of the generalizability of the models, and their limited interpretability. Federated learning, model-based deep learning, and semi-supervised learning are then recommended as potential solutions to these challenges in future research. [Display omitted] • Research related to deep learning technologies for shield tunneling is critically reviewed and analyzed qualitatively. • Typical types of monitoring data and three main application scenarios in shield tunneling are summarized and listed. • Performance of deep learning models and related challenges are analyzed for shield tunneling. • Future research opportunities for existing challenges are proposed for deep learning models in shield tunneling. [ABSTRACT FROM AUTHOR]

Real-time shield tail clearance measurement and monitoring is a key task during shield tunneling construction. The shield tail clearance measurement and monitoring technology development is still in its infancy, the current methods are mainly designed manually based on intuition. In order to fill the gap between the requirement of shield tail clearance measurement and monitoring and the limitations of the current methods, this paper systematically studies the existing mechanisms related to shield tail clearance measurement and monitoring, and develops a high-precision intelligent monitoring system for shield tail clearance. The proposed monitoring system includes four components: 1) two types of shield tail clearance calculation models, 2) the integrated hardware of the monitoring system which is composed of a data acquisition unit, a signal transmission unit and a control unit, 3) the region of interest (ROI) extraction method based on deep neural network, and the image processing algorithms for image enhancement and feature extraction, 4) the custom-developed software built on mature integrated development environment (IDE). After the calculation model of shield tail clearance is established, the system uses monitoring devices equipped with industrial cameras to obtain the on-site image, and then applies image processing technologies along with deep learning approach to extract the key features, which are brought into the model to calculate the values of shield tail clearance, finally displays these values and simulates the current tunneling attitude of the shield machine in real time. The experimental results show that the system proposed in this paper achieves the goal of high precision measuring and real-time monitoring of the shield tail clearance. • A high-precision intelligent monitoring system for shield tail clearance is proposed and invented. • Two types of shield tail clearance calculation models are presented. • The ROI extraction is taken as an object detection problem and solved by an end-to-end solution. • Comprehensive algorithms of image processing are used for image enhancement and feature extraction. • The high accuracy, stability and real-time performance of the system have been confirmed through experimental testing. [ABSTRACT FROM AUTHOR]

Tunnel face stability constitutes a significant challenge for shield tunneling in urban areas. The use of a slurry pressure balanced shield machine in mixed ground conditions containing mudstone is generally disturbed by clogging, which results in noises and large fluctuations in the tunnel face pressure. These fluctuations add difficulties to the prediction of the slurry pressure. This paper proposes a denoising method to overcome this difficulty. This method is combined with variational mode decomposition and detrended fluctuation analysis. The method is coupled with cross-correlation analysis (CCA) and a long short-term memory (LSTM) network to predict the tunnel face pressure using both tunneling parameters and geological data as input. The paper proposes a predictive strategy that separates the trend component and fluctuation component from the denoised tunneling data via CCA. Two LSTM-based predictors are presented and combined for the development of a new modeling strategy. The performances of the proposed strategy are illustrated through an application to the Nanning metro. The LSTM model with the proposed strategy gave excellent results in both mudstone and round gravel grounds with an overall R 2 value of 0.9974. The paper also presents a comparison of the proposed model with some traditional models as well as a discussion on the importance of input features in different ground conditions. • Use of a deep learning model for the prediction of a highly – fluctuated tunnel face pressure in SPB shield machine. • The model uses a predictive strategy that separates the trend component and fluctuation component. • Features of different components are illustrated in tunneling in gravel and mudstone grounds. [ABSTRACT FROM AUTHOR]

Complete and high-quality deformation monitoring data are critical for shield tunnel construction safety and quality. In engineering practices, data missing frequently occurs during instrumentation, adversely impacting further analysis and decision making. Existing imputation methods either ignore the crucial interactions between different parameters during shield tunneling or focus on the global characteristics of deformation data while neglect their local difference. This paper proposes a novel hybrid model, MCCB, combining multi-view matrix completion algorithms, convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) algorithms to impute missing deformation values in shield tunnel monitoring data. The performance of the proposed method is verified using bridge deformation data from a practical project in Beijing. Different missing patterns of the bridge deformation data are filled. The experiment results show that the proposed model can effectively learn the various characteristics of the deformation data and outperforms the four selected models and its two sub-models, and can be used to improve the accuracy of the deformation prediction through data imputation. The novelty of this study includes two aspects. First is that the complicated interaction between different parameters and local difference of the data are considered simultaneously. They have not been addressed before by existing studies. Second is that the innovative combination of matrix completion and deep learning algorithm for application in missing deformation values imputation. To our best knowledge, no research on engineering construction has implemented this technique before. [ABSTRACT FROM AUTHOR]

This paper investigates the prediction of geological conditions ahead of tunnel boring machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, the spatial and temporal features from TBM parameters and geological information are extracted for accurate prediction. The results from the case study indicate that (1) The proposed approach provides estimation with a high accuracy of 0.9986; (2) The past geological information has a significant contribution to the model; (3) The proposed approach outperforms several state-of-the-art methods including support vector machine (SVM), extreme gradient boosting (XGBoost) and LSTM method. The proposed hybrid deep learning approach can be a useful tool that provides reliable estimation of the advanced geological conditions in real-time. • A novel deep learning approach is proposed for advance geological perception. • The dependencies among the TBM parameters and geological information are considered. • A deep-learning framework that incorporates GCN and LSTM is established. • A realistic tunnel project in Singapore is used to demonstrate as a case study. • A high prediction accuracy can be achieved with average accuracy of 0.9986. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

In tunnel construction projects, delays entail high costs. Thus, tunnel boring machine (TBM) operators aim for high advance rates without compromising safety, a difficult mission in uncertain subterranean environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with significant practical relevance. In this paper, we present an intelligent decision support system developed in three steps. First, we propose an optimality score to evaluate TBM operation performance, taking into account the advance rate and the working pressure safety. A deep learning model then learns the mapping between the TBM measurements and this optimality score. Finally, in the context of a real application, the model provides incremental recommendations in order to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on a real micro-tunnelling project and demonstrates great promise for future applications. • An intelligent decision support system for TBM operation is proposed • An optimality score function quantifying the TBM operation efficiency is proposed • A deep learning model learns a mapping between TBM measurements and optimality score • An algorithm provides incremental recommendations given current operating conditions • Recommendations analyzed by experts show the approach potential and transferability [ABSTRACT FROM AUTHOR]

• The interpretation of GPR images depends heavily on the personal experience of the engineer. • Deep learning neural network can effectively extract tunnel disease features. • Data augmentation can be used to improve the training performance of deep learning methods. • The generalized intersection over union optimizes the regression evaluation standard. • The guide anchoring mechanism is added to optimize the regional recommendation network. In tunnel construction engineering, the form of tunnel void diseases are complex and easily affected by the geographical environment. The traditional manual interpretation of image data has the characteristics of heavy workload, high probability of missing, and misjudgment. This paper constructs a convolution neural network that integrates the mechanism of guiding anchoring to detect tunnel voids. The network is composed of four parts: Feature extraction network extracts disease features from the enriched samples; Region proposal by guided anchoring join the generalized intersection over union (GIoU) evaluation criteria, and predict the shape of the anchor point through learning; The obtained feature maps are fixed in the region of interest pooling; Finally, the disease features are classified and bounding box regression. Compared with the existing target detection algorithm, the experimental results show that the improved network achieves an average classification accuracy of 92.74%, and the trained model has good generalization ability and robustness. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes an image-based method for automated rock fracture segmentation and fracture trace quantification. It is integrated using a CNN-based model named FraSegNet, a skeleton extraction algorithm, and a chain code-based polyline approximation algorithm. A rock tunnel fracture database with a total of 3,000 images of rock tunnel faces is established and selected to train and test the FraSegNet model. A comparison study is further conducted and shows that the FraSegNet model shows advanced performance in pixel-level fracture trace map extraction and noise reduction compared to other deep learning approaches and traditional image edge detection algorithms. Next, the skeletons of the predicted fracture trace maps are extracted and the corresponding polyline for each fracture skeleton is thus obtained and output as a text file composed of key nodes coordinates. The fracture trace characteristics (trace length, dip angle, density, and intensity) are acquired using node-based files. The quantitative evaluation of the proposed method illustrates that it can extract trace occurrences effectively and accurately. A case study of three full scale tunnel sections demonstrates the proposed method to be an efficient approach for acquiring and evaluating 2D fracture occurrences of under-construction rock tunnel faces. [Display omitted] • A CNN-based model was developed for automated fracture trace map extraction. • Achieved automated evaluation of fracture trace statistics via an integrated approach. • Evaluation indexes were defined to assess the performances of the proposed method. • Field applicability was demonstrated with three full scale tunnel sections. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]