To address the issues of weak correlation between multi-view features, low recognition accuracy of small-scale targets, and insufficient robustness in complex scenarios in underground pipeline detection using three-dimensional ground penetrating radar (3D GPR), this paper proposes a three-dimensional pipeline intelligent detection framework that integrates multi-strategy improved deep learning technology. This paper explores a novel pathway to achieve accurate 3D localization through lightweight joint analysis of multi-view 2D GPR images. First, based on a B/C/D-Scan three-view joint analysis strategy, a three-dimensional pipeline three-view feature evaluation method is established by cross-validating forward simulation results obtained using time-domain finite difference (FDTD) methods with actual measurement data. Second, the DCO-YOLO framework is proposed, which integrates DySample dynamic upsampling, Convolutional gate linear unit (CGLU), and OutlookAttention cross-dimensional correlation mechanisms into the original YOLOv11 algorithm, significantly improving the small-scale pipeline edge feature extraction capability. Furthermore, a 3D-DIoU spatial feature matching algorithm is proposed, which integrates three-dimensional geometric constraints and center distance penalty terms to achieve automated association of multi-view annotations. The three-view fusion strategy resolves inherent ambiguities in single-view detection. Experiments based on 100 kilometers of real urban underground pipeline data show that the proposed method achieves accuracy, recall, and mean average precision of 96.2%, 93.3%, and 96.7%, respectively, in complex multi-pipeline scenarios, which are 2.0%, 2.1%, and 0.9% higher than the baseline model. Ablation experiments validated the synergistic optimization effect of the dynamic feature enhancement module, and Grad-CAM++ heatmap visualization demonstrated that the improved model significantly enhanced its ability to focus on pipeline geometric features. This study integrates deep learning optimization strategies with the physical characteristics of 3D GPR, offering an efficient and reliable novel technical framework for the intelligent recognition and localization of underground pipelines.

The superconducting electrodynamic suspension system has many advantages such as no active control, large clearance, and small weight, but its insufficient vertical damping is a problem that needs to be solved. A novel design of the active damping system is proposed, so the vertical damping force can be generated by the interaction between the energized traction coils and the active damping coils. By decomposing the superconducting electrodynamic suspension system into suspension system, traction system, and active damping system, and establishing mathematical models for electromagnetic force calculation in each subsystem, the working mechanisms of each subsystem are well analyzed. Furthermore, based on genetic algorithm, the size and installation position of the active damping coils are optimized, and the optimization result is consistent with the theoretical analysis of the active damping system. A co-simulation platform including electromagnetic force calculation and vehicle dynamics is built, and the co-simulation analysis verified that the proposed active damping system has good suppression effect on vertical and pitch angular motion of the vehicle.

The stable skeletal structure formed by the interlocking mechanism of the spatial movement of aggregate particles during compaction is the mechanism by which asphalt pavements are compacted and shaped. A major problem facing conventional compaction monitoring is that lacking the ability to monitor particle movement and a real-time evaluation method for pavement compaction, which can easily lead to problems such as uneven compaction and over-compaction. In addition, vehicle speed is an important parameter for analysing the dynamic response of a pavement. Current vehicle speed monitoring relies on complex field equipment and the collected vehicle speed parameters are difficult to match with other sensors for fusion analysis. In this paper, a method for monitoring the compaction quality of asphalt pavements and a method for collecting vehicle speed is proposed based on intelligent aggregate and aggregates interaction mechanisms. The experimental results show that with the continuous action of the compaction machinery, the compaction state of the asphalt pavement can be reliably captured and monitored by studying the spatio-temporal movement pattern of the intelligent aggregates. Meanwhile, the analysis of the time nodes of intelligent aggregate attitude change under the action of vehicle load allows accurate acquisition of vehicle speed parameters. Therefore, the method proposed in this study can improve the accuracy of asphalt pavement compaction state control as well as the accurate acquisition of vehicle speed. It can provide a reference for the later analysis of the dynamic response of pavement loads and the intelligent compaction of pavements.

Superconducting (SC) electrodynamic suspension (EDS) has a wide application prospect due to the advantages of no active control, self-stability and large gap, especially in the ultra-high speed maglev, rocket launching, Electromagnetic Aircraft Launching System and other high speed fields. SC coils and null-flux Ground Coils which are the primary and secondary windings respectively are applied in a typical EDS system, in that the null-flux ground coils provide levitation and guidance force. The calculation of electromagnetic forces that has been extensively studied by scholars all over the world is the basis of the design and optimization of the system. However, due to the limitation of complex structure, the calculation formula is often so complicated that the results have to be obtained by means of finite element method and numerical simulation. This paper aims to derive an analytical calculation of electromagnetic forces for SC EDS system based on some reasonable assumptions. The experimental data of MLX01 on the Japanese Yamanashi testline was used to verify the calculation model of this paper. In order to get a further validation, a small EDS rotary table was built based on null-flux ground coils and permanent magnets. The results of the experiment confirms the effectiveness of the proposed analytical calculation model.

During the long-term service of asphalt pavement, under the combined action of repeated loads and environmental factors, asphalt pavement will gradually appear small damage. When the damage accumulates to a certain extent, the asphalt pavement will have serious rutting deformation, resulting in poor highway flatness and greatly reducing the service life of the asphalt pavement. The traditional monitoring methods of pavement rutting deformation need high environmental detection conditions. At the same time, there are some problems, such as subjective judgment standard and unable to monitor in real time. The commonly used road surface coring detection methods will destroy the original continuous structure of the pavement and easily cause internal damage of the pavement under the action of rain and snow environment. In this study, based on the intelligent aggregate (IA) equipped with high-precision attitude sensor, a new pavement rutting deformation monitoring method was proposed. Combined with the indoor rutting experiment, the relationship model between intelligent aggregate attitude change and rutting deformation was established, and the relationship model between real pavement rutting deformation and intelligent aggregate attitude change was established by finite element method, so as to realize the rutting deformation monitoring method based on Intelligent aggregate.