To solve the problem that existing methods have difficulty in accurately obtaining the spatiotemporal distribution of vehicle loads on bridges in complicated traffic scenes, a spatiotemporal location identification method for vehicle loads based on multi-view information fusion is proposed. First, the vadYOLO-StrongSORT model is developed to detect and track vehicles simultaneously in a single view. Furthermore, based on image calibration and cross-view vehicle matching, an adaptive weighted least squares method is used for multi-view information fusion to correct the vehicle trajectory. Finally, the spatiotemporal distribution of axle loads is reconstructed by combining vehicle trajectories with axle configurations. The performance of the proposed method under typical traffic conditions is evaluated using model tests. The results show that the multi-view information fusion method significantly improves tracking stability, localization accuracy, and anti-occlusion performance compared with the single view-based vehicle location identification method. In the lane-changing scenes, the highest average localization error of the proposed method is less than 2.0 cm, which is significantly better than the 17.0 cm of the single-view method. In multivehicle occlusion scenes, the proposed method achieves a vehicle capture rate of up to 100%, compared with a maximum of only 72.5% for the single-view method. Meanwhile, vadYOLO-StrongSORT achieves the highest identification accuracy in the experiment compared with other detection and tracking models.

To explore the design and safety performance of super high-rise connected structures under the combined action of multiple disasters, taking Suzhou Supertall as an example, a vulnerability analysis is conducted under the combined earthquake-wind actions. First, the structure’s finite element model is established. Then, vulnerability assessments are conducted under individual earthquake and combined earthquake-wind actions. Finally, the response law of the structure is obtained. Results indicate that when exposed to combined earthquake-wind actions, the structure’s vulnerability increases with the earthquake and wind intensities, and the seismic action dominates structural damage. The probabilities of moderate, severe, and collapse damages are higher under the combined earthquake-wind actions than those under individual earthquakes. When the wind speed reaches 40 m/s, the probabilities of the structure reaching three failure states under rare earthquakes are 99.77%, 91.56%, and 46.54%, respectively, representing an increase of 1.11%, 10.73% and 14.65% compared with those under rare earthquakes alone and an increase of 0.27%, 6.26% and 14.34% compared with those of a typical high-rise connected structure under the same combined action of disasters.

To investigate the performance of Lamb wave monitoring of fatigue cracks at the floor beam cutout of orthotropic steel bridge decks, two full-scale specimens were produced and subjected to cyclic loading. The cutout cracks initiated during the tests were continuously monitored by Lamb wave sensors. Crack growth was estimated based on standardized wave features extracted from the received waves. Finite element models with typical regions were also established and validated by the experimental results. Parametric studies were conducted to determine the optimal sensor arrangements for monitoring cutout cracks, considering various parameters, such as crack lengths and sensor locations. The experimental results indicate that the standardized wave features increase when cracks and wave propagation paths are perpendicular, whereas the standardized wave features decrease when cracks and wave propagation paths are parallel. The parametric results reveal the optimal sensor arrangements for crack monitoring in the floor beam cutout regions, i.e., prearranging one excitation sensor and four reception sensors within the span of a typical floor beam cutout region. The excitation sensor should be placed at a distance of 50 mm from the cutout, whereas the reception sensors should be arranged at a distance of 50 mm from the cutout or 50 to 100 mm from the deck plate.

To enable accurate vessel recognition for bridge collision avoidance and early warning, an image dataset for vessels in bridge channels is established using cameras and data augmentation. This dataset includes complex scenarios such as long distances, multiple targets, and low visibility. Subsequently, the you-only-look-once version 5(YOLOv5)model is employed as the basic detector, and several modifications are applied to its network structure. Key enhancements involve replacing C3 modules in the backbone network with C2f modules, integrating the squeeze-excitation attention mechanism into the feature fusion network, and optimizing the prior anchors of the dataset using the K-means++ clustering algorithm. Finally, the modified model undergoes training and validation using PyTorch as the deep learning framework. Results demonstrate that the mean average precision for crucial vessels in the modified YOLOv5 model reaches 99.4%, representing an 11.1% improvement compared to the original YOLOv5 model. Additionally, the inference speed is measured at 102 frame/s. The established YOLOv5 model is a reliable and efficient cornerstone for warning against vessel-bridge collisions in complex navigable scenes.

To accurately predict the settlement of soft soil foundations under graded surcharge preloading, the Asaoka method was improved. Considering the change in the soil consolidation coefficient with load, the slope of the settlement prediction line was modified, and a prediction calculation method for the prediction line intercept was used. The improved Asaoka method considered the influence of consolidation stress on the consolidation coefficients and nonlinearity of soil consolidation. The reliability of the improved method was then verified through indoor consolidation tests and field observation. The results demonstrate that the consolidation coefficient C_v is not a constant, and it satisfies the relationship with the consolidation pressure P: C_v=aln(blnP), which has an error of less than 5%. The settlement prediction lines at various stress levels are not parallel, and the improved method exhibites a lower error rate than the original Asoaka method. The improved Asaoka method offers higher prediction accuracy than the traditional method and can reliably predict the settlement of soft soil foundations during graded surcharge preloading.

Traditional machine vision detection methods suffer from low accuracy in identifying small-scale defects. To address this, a nondestructive identification method for steel surface defects is proposed based on an enhanced version of the fifth version of the You Only Look Once(YOLOv5)algorithm. In this improved approach, the Res2Block module is incorporated into the backbone of the YOLOv5 algorithm to expand the receptive field and improve computational efficiency. Additionally, the recursive gated convolution structure is fused into the neck of the YOLOv5 algorithm to further enhance the computational performance of the surface defect identification method. To validate the effectiveness of the proposed method, a series of ablation experiments were conducted using different module combinations. These results were then compared with those obtained through other object detection methods. This comparison reveals that the proposed method achieves a mean average precision of 67.8% and an F₁-score of 86.0% in steel surface defect identification. When compared with the original YOLOv5 algorithm, the proposed method exhibits superior performance, particularly in the identification of small-scale steel surface defects. Furthermore, it also surpasses other object detection methods, such as SSD, YOLOv3, YOLOv5-Lite, and YOLOv8, demonstrating significant improvements in computational accuracy.

Through comprehensive data collection, along with the coarse aggregate mechanical index, fractal dimension, and British pendulum number(BPN), a pavement friction prediction model was proposed on the basis of backpropagation neural networks(BPNNs)and support vector machine(SVM). An accelerated attenuation test was conducted to examine the antiskid performance of the asphalt mixture and aggregates at different wearing cycles. Subsequently, BPN was fitted using an exponential model. Gray relational and correlation analyses were performed to evaluate the factors influencing pavement skid resistance. According to the principal component analysis results, six schemes were prepared for the training, validation, and testing of BPNN and SVM algorithms. Test results indicate that different aggregates exhibit different antiskid properties. Quartz sandstone is the most suitable, followed by basalt and limestone. The polished stone value has the highest correlation with the attenuation model of asphalt antiskid performance. BPNN is more stable, with an R² value of approximately 0.8.

To facilitate the contact analysis of involute cylindrical gears, a meshing contact analysis and tooth profile modification algorithm program for involute cylindrical gears is designed. The involute spur gear parameter equation is employed to accurately model the radius of curvature of the tooth profile contact point, and the Hertz contact formula is enhanced to solve the gear meshing contact stress considering the actual load. To reduce the impact of gear meshing, five tooth profile modification methods and their contact stress analysis methods after modification are given in the algorithm program. Compared with the calculations by MASTA, the industry-recognized gear design simulation software, the algorithm program has a higher accuracy in solving the contact stress and the contact stress distribution after tooth profile modification accords with the MASTA calculation results, and the average error is about 5.04%, which confirms the rationality of the algorithm program.

To solve the problem of heavy artifacts in ultrasonic images, a novel ultrasonic imaging method is presented using a sum of sinc(SoS)kernel for eliminating the artifacts caused by the diffusion of isacoustic path, signal tail, or noise simultaneously. First, the envelope of ultrasonic echo is obtained and passed through a SoS kernel, then the signal is sampled at equal intervals determined by the echo signal information degree, and the Fourier transform is applied to the discrete sampling data to obtain the Fourier coefficient sequence. After that, the spectral estimation algorithm is used to estimate the parameters of the ultrasonic echo signal and reconstruct the echo signal using the estimated parameters. Finally, the ultrasonic image is obtained by calculating the acoustic field using the reconstructed echoes. Experimental results show that the image artifacts are effectively removed using focused and straight probes to test straight slot defects and through-hole defects, respectively. Compared with the B-scan images, the peak signal-to-noise ratios reach 24.306, 23.213, 15.074, and 16.444 dB, and the structural similarity indexs are 0.931, 0.932, 0.746, and 0.773, which indicates that the quality of the defect images is greatly improved using the proposed method.

To tackle the issue of high cost and large volume for offshore wind power generators, a novel dual-rotor dual-stator permanent magnet synchronous generator(DRDS-PMSG)is proposed. The equivalent magnetic circuit model of the generator is established, finite element analysis is performed to evaluate the electromagnetic characteristics and coupling effect, and some simulation results are verified through experiment. The simulation analysis results show that three typical equivalent magnetic circuits exist with changed relative positions between the inner and outer magnets, and the equivalent reluctance of the coupling region can be described using a coupling coefficient. The coupling effect of inner and outer machines is revealed by electromagnetic characteristics, including cogging torque and electromagnetic torque. The peak-to-peak values of the cogging torque of inner and outer machines are 0.52 and 1.64 kN·m, the average values of electromagnetic torque are 11.65 and 27.09 kN·m, and the torque ripples are 6.02% and 4.12%, respectively. In general, a coupling effect exists between the inner and outer machines; however, the coupling effect is effectively reduced by the flux barrier.

To investigate and design an efficient blind detection method for third-party scenarios, a third-party efficient physical downlink control channel(PDCCH)blind detection method was proposed based on polar decoding metric selection. This method comprised two main components: the study of the polar decoding algorithm, which introduced a polar decoding metric based on downlink control information(DCI)length and proposed an improved third-party blind detection method based on polar decoding metric selection; and the investigation of the PDCCH blind detection algorithm, which introduced a reordering blind detection algorithm. The enhanced polar decoding algorithm and reordering blind detection algorithm were organically combined to present an efficient PDCCH blind detection method for third-party scenarios. The proposed method was validated and analyzed using a 5G PDCCH blind detection simulation link on the MATLAB platform. The results show that the proposed method effectively reduces the number of PDCCH blind detections and the count of DCI candidates while enhancing blind detection efficiency and ensuring target capture accuracy.

Three pricing strategy models—free, charge, and cash subsidy—are constructed for content platforms in a multilateral market based on the game theory. The optimal pricing strategy for a platform is identified by comparing the parameters under each pricing strategy. The results reveal that ad interference cost and ad marginal revenue affect a platform’s pricing strategy selection and the cash subsidy amount. The cash subsidy strategy is used when both are within a certain range of thresholds; the charge strategy is adopted when the ad interference cost is very high; and the free strategy is adopted in other cases. In addition, under the cash subsidy strategy, the amount of cash subsidy is negatively correlated to ad interference cost and positively related to ad marginal revenue. Under the same conditions, adopting the cash subsidy strategy is better for all stakeholders and social welfare than the other two pricing schemes. Moreover, ad marginal revenue affects some parameters in the cash subsidy strategy and the free strategy in opposite directions.