At present, the performance of the traditional centralized coil antenna used for radio frequency identification(RFID) detection and localization of underground cables is insufficient, which seriously restricts the improvement of its detection and localization distance. A new type of high field strength distributed RFID coil antenna structure was proposed. Based on the derivation of antenna related electrical parameters, the magnetic field strength of the coil antenna was taken as the objective function, and its quality factor was fixed as the constraint condition. Particle swarm optimization algorithm was employed to optimize the number of turns of the coil antenna and the turn spacing between adjacent two turns. Finally, an experimental test platform was built. The test results show that compared with the traditional centralized RFID coil antenna, the distributed RFID coil antenna increases the reading distance by 33.3%, significantly enhances the received signal strength indicator (RSSI) at the same distance, and helps to improve the accuracy of the underground RFID localization method based on RSSI, which provides an important reference for the application of RFID detection and localization of underground cables.

During growth, crop stalks are prone to bending deformation, posing challenges for computer visualization simulation. A differential analysis method was employed to investigate the physical stress-strain relationship of stalks, and a visualization simulation method was proposed for flexible stalks. Firstly, a mechanical model of the stalk under tensile, bending, shear, and torsional loads was established. Secondly, a geometric model based on a semi-structural approach and surface modeling was constructed. Finally, C++ and OpenGL were utilized to implement the visualization simulation. Experimental analysis of the bending stress-strain characteristics of stalks from different varieties was conducted. The results demonstrate that this method can relatively accurately simulate the deformation process of stalks. The physics-based model ensures the accuracy of bending simulation, providing a novel informatics analysis tool for selecting and breeding lodging-resistant crop varieties.

In order to study the effect of basalt fiber on the porosity of recycled concrete under salt erosion, the internal variation of concrete was analyzed by scanning electron microscopy, and a linear regression model was established between the porosity of concrete and the number of dry and wet cycles, the height of penetration, the compressive strength and the splitting tensile strength. A fully connected neural network (FCNN) model was established for the penetration height under different soaking ages and fiber content for predicting the corrosion resistance life of basalt fiber regenerated concrete under salt erosion conditions. The results show that the porosity of concrete increases gradually with the erosion age increasing, and the appropriate addition of basalt fiber can significantly reduce the porosity of concrete, and the improvement effect is most significant when the fiber content is 1.0%. With fiber content increasing, the salt permeability resistance of recycled concrete increases, and permeability height decreases. When the fiber content is 1.0%, the best compressive strength and splitting tensile strength are obtained. The fully connected neural network model has a good effect and provides a reliable reference for the life prediction of basalt fiber recycled concrete.

In order to study the mechanical properties of negative Poisson’s ratio(NPR) anchor cable under static tension and drop hammer impact load, the self-developed NPR anchor cable tension test system and NPR anchor cable drop hammer impact test system were used to conduct static tensile test and dynamic impact test for a certain batch of NPR anchor cables. Its resistance and absorption of slow deformation and instantaneous impact energy of surrounding rock were verified by the supporting force, elongation and expansion of anchor cable. Secondly, a 3D numerical model of NPR anchor cable was established, and relevant numerical parameters were calibrated according to laboratory test results. The static tensile test of NPR anchor cable and the drop hammer impact test were carried out. The results of the numerical test are in good agreement with those of the laboratory test, which verifies the reliability of the numerical model. The 3D numerical model of NPR anchor cable can be used as an auxiliary analysis tool for the improvement and upgrading of NPR anchor cable in the future.

1g shaking table test refers to the shaking table test carried out under conventional gravity conditions, which is the mainstream type of shaking table test at present. A distinction was made between strict similarity models and strain distortion models, primarily utilizing the control equation method with supplementary dimensional analysis. The similarity relationships between these models and the prototype during different deformation stages in rock slope 1g shaking table tests were theoretically derived, and the key parameters required to achieve similarity between the model and the prototype were established. Numerical tests were conducted to verify and evaluate the established similarity relationships for different deformation stages, considering the inherent vibration characteristics and dynamic response characteristics of rock slopes. The results indicate that both strict similarity models and strain distortion models can accurately calculate the natural vibration characteristics of the prototype slope and the dynamic response characteristics during the elastic deformation stage under seismic loads. However, for large deformation issues, it is recommended that the strain similarity ratio be close to or equal to 1. These research findings provide valuable references for the design of similarity relationships and the selection of similarity models in 1g shaking table tests for rock slopes.

In order to explore the impact of the number of bearing plates on the bearing capacity of the expanded pile and the soil around the pile, a small half face pile model test equipment developed was used to record the deformation of the soil around the pile in real-time using using digital image correlation (DIC) technology equipment, and to analyze the displacement and failure characteristics of the soil around the pile. Experimental research shows that during loading, the soil under the load-bearing plate is compressed to form a compression zone, and the influence range of this zone gradually increases with the increase of load, while squeezing towards the soil on both sides. The cracks above the bearing plate continue to develop until a free zone is formed, and this area expands with the increase of load. The bearing capacity of three plate piles has been significantly improved compared to single plate piles and two plate piles, but their load-settlement curve shapes are relatively similar. During the loading process, the displacement changes of the soil around the pile near and below the bearing plate are the most significant. Therefore, the study supplements the research on the impact of the number of bearing plates on the soil around piles and other related directions.

Based on Monte Carlo method, Python and Abaqus interface were used for secondary development, and an interfacial transition zone was generated to distinguish between natural coarse aggregates and new and old interfacial transition zone(ITZ) and recycled aggregate concrete (RAC) 2D meso-five phase model of new and old mortar. An improved moisture-chloride ion coupling model under dry-wet cycles was proposed, and the computational results of this model were compared and validated against physical experiments, with good agreement. This model was then applied to analyze the effects of dry-wet cycle periods, ITZ permeability, water-cement ratio, and natural aggregate volume fraction on chloride ion transport properties. The numerical results show that as the number of dry-wet cycles increases, the diffusion depth and concentration of chloride ions in RAC also increases. When the ratio of ITZ diffusion coefficient to the new mortar diffusion coefficient increases, the chloride ion concentration in the diffusion region increases significantly, especially at the front end of the diffusion zone. In addition, there is a positive correlation between RAC materials with different water-cement ratios and chloride ion transport capacity, with little variation in chloride ion transport performance within the high water-cement ratio range. Finally, the volume fraction of recycled aggregates has a significant impact on the chloride ion permeability of RAC, indicating that the ITZ and new and old mortar have an important influence on the transport of chloride ions.

Primary cracks and new cracks develop within the engineering rock mass, leading to the formation of macroscopic cracks. The hollow cylindrical discrete element simulation test enables the emulation of complex stress paths. In order to solve the problems existing in the simulation test of hollow cylindrical discrete element, such as numerous influencing factors and lengthy meso-parameter calibration, a method of mesoscale parameter calibration of hollow cylindrical sandstone discrete element based on machine learning algorithm was proposed. Through variations in input variables within the discrete element model, 210 sets of simulation data were obtained. A mesoscopic parameter calibration model based on random forest algorithm and extreme gradient boosting(XGBoost) algorithm was established, the prediction accuracy of the model was compared, the parameter sensitivity was analyzed, and the contribution of input parameters to the overall mechanical properties of the rock was quantified. Combined with the indoor triaxial test of hollow cylinder, the calibration results show that the XGBoost algorithm has the advantages of computing speed, and can quickly locate the range of discrete element mesoscopic parameters, which provides a new idea for the calibration of discrete element mesoscopic parameters of hollow cylinder, and has the value of engineering application.

The research on the factors affecting the convenience of urban integrated transportation transfer can provide scientific basis and guidance for urban transportation planning. Based on the comprehensive transportation theory and transfer convenience theory, the logic of the comprehensive transportation transfer system was sorted out, and the comprehensive transportation transfer system diagram was drawn. Then, from the two perspectives of the comprehensive transportation hub station and the transfer origin-destination(OD) pair of a single station, Pearson correlation analysis, geographic detector model and multiple linear regression model were used to study the factors affecting the convenience of integrated transportation transfer. The results show that the transfer convenience of a single hub station is mainly related to the location of the hub station. Geographical distance, travel mode and number of travel stations are the main factors of the shortest transfer time, and the number of travel stations is the most important factor. Geographical distance, shortest highway mileage, travel mode and number of travel stations are the main factors of the least transfer cost, among which the travel mode and number of travel stations are the most important factors.

In order to efficiently and accurately predict freight forwarders’ transportation modes preferences between China and Europe during major emergencies, as well as to uncover the relevant factors influencing freight forwarders’ choices, the stated preference method was employed to survey freight forwarders. Additionally, considering the influences of transportation and cargo attributes, decision trees, logistic regressions, and random forest prediction models were constructed to forecast the selection behavior of freight forwarders. The prediction results of the machine learning model and the discrete choice model were comprehensively compared through four evaluation metrics: accuracy, precision, recall, and F₁ score. Furthermore, the random forest algorithm was utilized to rank the importance of attributes influencing freight forwarders’ transportation mode choices during different stages of the pandemic. The study results demonstrate that the prediction accuracy of all three machine learning models is higher than that of the discrete choice model. Among them, the random forest model exhibits superior prediction accuracy compared to the decision tree and logistic regression models in addressing the choice of Sino-Europe container transport modes, making it more suitable for this problem. Regarding influencing factors, during stable periods, cargo attributes are identified as the most important factors. When major emergencies occur, freight forwarders place greater emphasis on the threshold delay time. Furthermore, the destination and value of the cargo are found to have significant impacts on the choice of Sino-Europe container transport modes. The study proposes an accurate analysis of the decision-making mechanisms guiding freight forwarders’ mode choice behavior during major global emergencies. Furthermore, it is utilized by shipping companies and operators of the China Railway Express to gain a deeper understanding of the preferences and decision-making factors influencing freight forwarders. The insights derived from this study are considered a solid basis for effectively responding to similar emergency situations.