Search Results (7)

In outdoor environments, GNSS is commonly used for vehicle navigation and various location-based ITS services. However, in GNSS shadow roads such as tunnels and underground highways, it is challenging to provide these services. With the rapid expansion of GNSS shadow roads, the need for radio positioning technology that can serve the role of GNSS in these areas has become increasingly important to provide accurate vehicle navigation and various location-based ITS services. This paper proposes a new GNSS shadow road radio positioning technology using multidirectional BLE beacon signals. The structure of a multidirectional BLE beacon that radiates different BLE beacon signals in two or four directions is introduced, and explains the principle of differential RSSI technology to determine the vehicle’s location using these signals. Additionally, the technology used to determine the vehicle’s speed is described. A testbed was constructed to verify the performance of the developed multidirectional BLE beacon-based radio navigation system. The current status and future plans of the testbed installation are introduced, and the results of position and speed experiments using the testbed for constant speed and deceleration driving are presented. Full article

The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the Federal Highway Administration of the United States to predict the service states of the structural, civil, and non-structural sections of a tunnel, respectively. The results indicate that ensemble learning algorithms such as Light Gradient Boosting Machine (LGBM) and Random Forest outperform Support Vector Machine, Multi-Layer Perceptron, Decision Tree, and K-Nearest Neighbor in solving imbalanced classification problems presented in the NTI database. The machine learning models established using the LGBM algorithm exhibited prediction accuracies of 90.9%, 96.4%, and 77.3% for the structural, civil, and non-structural sections, respectively. The importance sorting of features influencing the tunnel’s service state was then performed based on the LGBM model, revealing that the features with a significant impact on the service states of the structural, civil, and non-structural sections are service time, tunnel length and width, geographic position (longitude and latitude), minimum vertical clearance, annual average daily traffic (AADT), and annual average daily truck traffic (AADTT). Data-driven LGBM models identified human factors such as AADT and AADTT as key features influencing the service states of tunnels’ structural sections, and these factors should be taken into consideration in further research to elucidate the potential physical mechanisms. Full article

During the long-term operation of tunnels, surrounding rock undergoes creep effects under environmental loads, resulting in changes in the aging evolution model of stress and deformation in surrounding rock and lining, which affects the long-term operational safety of the tunnel. Therefore, using the model test device for time-dependent characteristics of stress and deformation of weak surrounding rock and lining structure in operational tunnels, taking into account the influence of tunnel burial depth and lateral pressure coefficient of surrounding rock, a model test on time-dependent characteristics of stress and deformation in weak surrounding rock and lining structure was conducted, and the stress and deformation time-varying curves at key locations of surrounding rock and lining were obtained. The time characteristics of surrounding rock stress, the contact force between surrounding rock and lining, internal force, and displacement of lining structure were analyzed. Research findings indicate that the stress of surrounding rock, the internal force and displacement of lining structure, and the contact force between surrounding rock and lining all increase and tend to be stable over time under constant load. This implies that the stress and deformation of the surrounding rock and lining structure exhibit time-dependent changes. With changes in burial depth and lateral pressure coefficient, significant variations are observed in the various indicators of stress and deformation in the surrounding rock and lining structure, indicating both time-dependent and long-term characteristics in terms of stress and deformation. The research results provide basic data support for the study of the time-dependent characteristics of stress and deformation between weak surrounding rock and lining structures in operational tunnels and can provide theoretical and technical guidance for the long-term service status discrimination and disaster prevention and control of operational tunnels. Full article

A scientifically accurate assessment of tunnel health is the prerequisite for the safe maintenance and sustainability of the in-service road tunnel. The changing trend of tunnel health is not considered in existing research. Most evaluation indicators are static and the ambiguities or randomness at the boundary of the health level intervals is neglected in most evaluation methods. In this paper, the evaluation index system combined with dynamic, and static is set to solve these problems. The changing trend of the health state of tunnels is analyzed through the cubic b-spline function. The weights of evaluation indicators are calculated based on the AHP-improved entropy method. The health evaluation method is proposed based on combing the extension theory and the cloud model improved by the cloud entropy optimization algorithm. Finally, the evaluation results of the proposed method are compared with the detection data of the Beilongmen Tunnel of Zhangzhuo Expressway. The results demonstrate that 80% of the evaluation results in the sample tunnel data are consistent with the standard results, and the remaining 20% show a lower level of health than the standard results. This reflects the evaluation results are impacted by the trend of tunnel health status changes. The health state can be more accurately evaluated by dynamic indicators. The improved extension cloud model is feasible and applicable in the health assessment of tunnels. This work provides innovative ideas for the evaluation of the health state of tunnels and theoretical support for the formulation of reasonable maintenance measures. Full article

The status of zinc oxide (ZnO) arresters is directly related to the safety of power grids. However, as the service life of ZnO arresters increases, their insulation performance may decrease due to factors such as operating voltage and humidity, which can be identified through the measurement of leakage current. Tunnel magnetoresistance (TMR) sensors with high sensitivity, good temperature stability, and small size are excellent for measuring leakage current. This paper constructs a simulation model of the arrester and investigates the deployment of the TMR current sensor and the size of the magnetic concentrating ring. The arrester’s leakage current magnetic field distribution under different operating conditions is simulated. The simulation model can aid in optimizing the detection of leakage current in arresters using TMR current sensors, and the findings serve as a basis for monitoring the condition of arresters and improving the installation of current sensors. The TMR current sensor design offers potential advantages such as high accuracy, miniaturization, and ease of distributed application measurement, making it suitable for large-scale use. Finally, the validity of the simulations and conclusions is verified through experiments. Full article

Finding a low-cost and highly efficient method for identifying subway tunnel damage can greatly reduce catastrophic accidents. At present, tunnel health monitoring is mainly based on the observation of apparent diseases and vibration monitoring, which is combined with a manual inspection to perceive the tunnel health status. However, these methods have disadvantages such as high cost, short working time, and low identification efficiency. Thus, in this study, a tunnel damage identification algorithm based on the vibration response of in-service train and WPE-CVAE is proposed, which can automatically identify tunnel damage and give the damage location. The method is an unsupervised novelty detection that requires only sufficient normal data on healthy structure for training. This study introduces the theory and implementation process of this method in detail. Through laboratory model tests, the damage of the void behind the tunnel wall is designed to verify the performance of the algorithm. In the test case, the proposed method achieves the damage identification performance with a 96.25% recall rate, 86.75% hit rate, and 91.5% accuracy. Furthermore, compared with the other unsupervised methods, the method performance and noise immunity are better than others, so it has a certain practical value. Full article

The existing cutterhead torque calculation method usually simplifies the characteristics of the shield, which ignores the rolling angle. In this paper, the cross-river shield project of Wuhan Metro Line 8 is taken as the research focus. Firstly, the measured data of the cutterhead torque (CT), the rolling angle and rotation direction were analyzed. Then on this basis, the penetrability, tunneling thrust, and rolling angle were taken as the influential factors to analyze CT sensitivity. Finally, based on the theoretical calculation model, a modified solution of CT was obtained considering the rolling angle. The results show that the rolling angle can be reduced to zero by changing the direction of the cutterhead rotation; the rolling angle has a greater impact on CT than the other two factors as shown through the analysis of the range difference and Statistical Product and Service Solutions (SPSS) method. As the absolute value of the rolling angle increases, CT also increases, and the relationship between them is linear. To a certain extent, the rolling angle of the shield can reflect the difficulty of tunneling and the running status. By monitoring the rolling angle of the shield, the prediction of CT can be more in line with the actual construction conditions. Full article