Past investigations of the hydrodynamic forces on vertical columns have generally been based on rigid structure assumptions. The effects of structural flexibility and geometry characteristics on the hydrodynamic force distribution are not well understood. In this study, fluid-structure interaction models are developed for numerical analyses. This modeling technique is verified with an experimental test in the literature using both circular and rectangular cross-sections. A series of material elasticities that present structural properties ranging from rigid to flexible is then used to conduct analyses. This finding indicates that an increase in structural flexibility can decrease the impact force to some extent, but this effect is limited. A concrete bridge pier with fluid flow impact can be considered rigid when it is fixed at the bottom. After that, the effects of the initial downstream water height and the width of water tank on the hydrodynamic force are thoroughly investigated. The results demonstrate that the increase in the downstream water height with a constant upstream water height corresponds to a decreased force. Moreover, the vertical column results in a blockage effect on the fluid flow. The greater the blockage effect, the higher the hydrodynamic force. The blockage effect from the vertical column can be neglected when the tank width is greater than eight times the structural cross-section diameter.

To adapt to the unique demand-supply features of accessory parking lots at passenger transport hubs, a mixed parking demand assignment method based on regression modeling is proposed. First, an optimal model aiming to minimize total time expenditure is constructed. It incorporates parking search time, walking time, and departure time, focusing on short-term parking features. Then, the information dimensions that the parking lot can obtain are evaluated, and three assignment strategies based on three types of regression models—linear regression (LR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP)—are proposed. A parking process simulation model is built using the traffic simulation package SUMO to facilitate data collection, model training, and case studies. Finally, the performance of the three strategies is compared, revealing that the XGBoost-based strategy performs the best in case parking lots, which reduces time expenditure by 29.3% and 37.2%, respectively, compared with the MLP-based strategy and LR-based strategy. This method offers diverse options for practical parking management.

To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components, a method based on deep learning and computer vision is developed to identify the geometric parameters. The study utilizes a common precast element for highway bridges as the research subject. First, edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology. Subsequently, a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output. A dataset is generated by varying the control parameters and noise levels for model training. Finally, field measurements are conducted to validate the accuracy of the developed method. The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components, with an error rate maintained within 5%.

Conventional optimal sensor placement (OSP) methods employ the premise that all sensors work perfectly during long-term structural monitoring. However, this premise is often difficult to fulfill in real applications due to poor manufacturing and material aging of sensors, human damage, and electromagnetic interference. This paper presents a robustness-oriented OSP method that considers sensor failures. The OSP problem is designed with consideration of sensor failures to ensure that both complete vibration data collected by all sensors and incomplete vibration data caused by individual sensor failures can accurately identify structural modal parameters. A dispersion-aggregation firefly algorithm (DAFA), which is derived from the basic firefly algorithm, has been proposed to solve this complicated optimization problem. The dispersion and aggregation operators are designed to prevent falling into local optima and to rapidly converge to the global optima. The proposed methodology is confirmed by extracting the robust sensor configuration for a long-span cable-stayed bridge. The robustness of the optimal sensor configurations against sensor failure is thoroughly explored, and the performance of the proposed DAFA is extensively examined.

Postgrouting at the pile tip enhances the performance of cast-in-place piles. To clarify the performance of tip and side resistances, this study analyzed static load test data from two test piles before and after grouting. Mechanisms underlying an improvement in tip resistance and the influence of postgrouting on side resistance were investigated via theoretical analysis. Finally, a design method for tip resistance control via settlement was proposed. Results indicate that the ultimate bearing capacity of piles increases after grouting compared to before, underscoring the importance of tip grouting in gravelly soils and its profound impact on load transmission in pile foundations. Postgrouting at the pile tip enhances the strength as well as initial stiffness of the bearing stratum, ultimately elevating the overall pile foundation-bearing capacity. Additionally, tip grouting helps in strengthening overall side resistance, especially around the pile tip. The grouting procedure has an impact on the soil’s arching effect at the pile tip; the extent of the arching effect and an increase in horizontal tension close to the pile tip are positively correlated with the effectiveness of grouting reinforcement. The design method for tip resistance control via settlement based on measured data statistics was validated using engineering examples, and the method has a practical reference value.

The study aimed to address the issue of elevated residual stress levels in dissimilar girth welds of cast steel joints. To achieve this, the hybrid welding technology, which yields high welding speeds while simultaneously reducing residual stresses, has been introduced. This study utilizes a numerical simulation method to investigate the temperature and residual stress field in the hybrid welding of G20Mn5 casting-Q355 low-alloy steel welded pipe. A comparison of the findings of this study with those of other welding processes revealed the technological advantages of hybrid welding. The research outcomes show that due to geometric discontinuities and material differences, the temperature field of the joint exhibits uneven distribution characteristics, and the peak temperatures on the Q355 steel side exceeds those on the G20Mn5 steel side. An evident stress gradient is present in the residual stress field of the joint post-welding, with peak stress located at the weld root on the Q355 steel. Compared with arc welding, the hybrid welding leads to decreased residual stresses and deformation, with high stress outside the heat-affected zone diminishing rapidly. Furthermore, it significantly improves the welding efficiency. This study elucidates the distribution and underlying causes of thermal and residual stress fields in dissimilar girth welds. This serves as a foundation for the application of hybrid welding technology in welded cast steel joints.

Spiral pile foundations, as a promising type of foundation, are of significant importance for the development of offshore wind energy, particularly as it moves toward deeper waters. This study conducted a physical experiment on a three-spiral-pile jacket foundation under deep-buried sandy soil conditions. During the experiment, horizontal displacement was applied to the structure to thoroughly investigate the bearing characteristics of the three-spiral-pile jacket foundation. This study also focused on analyzing the bearing mechanisms of conventional piles compared with spiral piles with different numbers of blades. Three different working conditions were set up and compared, and key data, such as the horizontal bearing capacity, pile shaft axial force, and spiral blade soil pressure, were measured and analyzed. The results show the distinct impacts of the spiral blades on the compressed and tensioned sides of the foundation. Specifically, on the compressed side, the spiral blades effectively enhance the restraint of the soil on the pile foundation, whereas on the tensioned side, an excessive number of spiral blades can negatively affect the structural tensile performance to some extent. This study also emphasizes that the addition of blades to the side of a single pile is the most effective method for increasing the bearing capacity of the foundation. This research aims to provide design insights into improving the bearing capacity of the foundation.

To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies, this paper proposes a dynamic emergency evacuation sign system. A simulation platform integrating building information modeling (BIM) and virtual reality (VR) technologies was employed to create subway fire evacuation scenarios using both the current and proposed dynamic emergency evacuation signage systems. Through simulation experiments, fine-grained microscopic data on passenger behavior was collected. Seven indicators were selected to assess evacuation efficiency and wayfinding difficulty. The analysis explored the influence of evacuation signs on passenger behavior in both overall and decision-making areas, thereby validating the effectiveness of the new emergency evacuation signage system. The results show that the dynamic evacuation signage system significantly improves overall passenger evacuation efficiency and reduces decision-making errors. It also improves wayfinding efficiency in critical decision areas by reducing the need for direction identification, minimizing stopping times, and lowering the frequency of decision errors. The method for evaluating the effects of emergency evacuation signs on passenger evacuation behavior proposed in this study provides a robust theoretical basis for the design and optimization of emergency-oriented signs.

A self-centering bridge bent equipped with energy-dissipation (ED) beams is proposed. Quasi-static tests are conducted on self-centering bridge bents, both with and without ED beams, to validate the accuracy of the corresponding numerical models. The effects of various parameters, such as the web area of ED beams, prestressing force of tendons, tendon arrangements, and number of column segments, on the seismic performance of self-centering bridge bents with ED beams are evaluated using the validated numerical model. The results demonstrate that the numerical models accurately replicate the quasi-static test results, with average errors in the lateral force remaining below 9.6%. The web area of ED beams significantly affects the strength, cumulative energy dissipation, and relative self-centering index (RSI) of the self-centering bridge bents. Increasing the prestressing force enhances the lateral force and self-centering capability of the bridge bents but has minimal effect on their ED capacity. Reducing the number of segments in each column enhances the lateral force and cumulative hysteretic energy dissipation of the self-centering bridge bents while exerting an insignificant effect on the RSI. Thus, the proposed novel system is highly suitable for double- or multicolumn piers supporting bridges in regions prone to strong earthquakes.

Food waste, owing to its high organic content and moisture, offers a more scientifically sound resource utilization method compared to traditional treatment processes. This study presents a method to convert food waste into nitrogen-doped, modified hydrogel biochar modified food waste hydrogel biochar and investigates its effectiveness in adsorbing humic acid (HA). The modified biochar demonstrates superior adsorption capacity for HA compared to unmodified biochar. The adsorption follows the Langmuir isotherm model (R² = 0.999), achieving a maximum adsorption capacity of 49.5 mg/g with R_L = 0.001 3-0.005 1 (0 < R_L < 1). Furthermore, the adsorption process conforms to a pseudo-first-order model. The mechanism underlying HA adsorption involves the successful modification of food waste hydrogel biochar by 3-Aminopropyltriethoxysilane (APTES). This modification forms Si—R—$\mathrm{NH}_{3}^{+}$ on the biochar surface, which interacts with the COOH— groups in HA through hydrogen bonding and coordination bonds. Some unmodified APTES directly adsorbs onto the biochar surface, undergoing condensation and self-assembly to form ladder-like oligomeric siloxane polymers that enhance HA adsorption.

Rail positioning is a critical step for detecting rail defects downstream. However, existing orientation-based detectors struggle to effectively manage rails with arbitrary inclinations and high aspect ratios, particularly in turnout sections. To address these challenges, a fuzzy boundary guidance and oriented Gaussian function-based anchor-free network termed the rail positioning network (RP-Net) is proposed for rail positioning in turnout sections. First, an oriented Gaussian function-based label generation strategy is introduced. This strategy produces smoother and more accurate label values by accounting for the specific aspect ratios and orientations of the rails. Second, a fuzzy boundary learning module is developed to enhance the network’s ability to model the rail boundary regions effectively. Furthermore, a boundary guidance module is developed to direct the network in fusing the features obtained from the downsampled network output with the boundary region features, which have been enhanced to contain more refined positional and structural information. A local channel attention mechanism is integrated into this module to identify critical channels. Finally, experiments conducted on the tracking dataset show that the proposed RP-Net achieves high positioning accuracy and demonstrates strong adaptability in complex scenarios.

Fully implanted brain-computer interfaces (BCIs) are preferred as they eliminate signal degradation caused by interference and absorption in external tissues, a common issue in non-fully implanted systems. To optimize the design of electroencephalography electrodes in fully implanted BCI systems, this study investigates the penetration and absorption characteristics of microwave signals in human brain tissue at different frequencies. Electromagnetic simulations are used to analyze the power density distribution and specific absorption rate (SAR) of signals at various frequencies. The results indicate that lower-frequency signals offer advantages in terms of power density and attenuation coefficients. However, SAR-normalized analysis, which considers both power density and electromagnetic radiation hazards, shows that higher-frequency signals perform better at superficial to intermediate depths. Specifically, at a depth of 2 mm beneath the cortex, the power density of a 6.5 GHz signal is 247.83% higher than that of a 0.4 GHz signal. At a depth of 5 mm, the power density of a 3.5 GHz signal exceeds that of a 0.4 GHz signal by 224.16%. The findings suggest that 6.5 GHz is optimal for electrodes at a depth of 2 mm, 3.5 GHz for 5 mm, 2.45 GHz for depths of 15-20 mm, and 1.8 GHz for 25 mm.

The axial field hybrid permanent magnet memory machine (AFHPM-MM) employs a hybrid permanent magnet excitation combining NdFeB and AlNiCo, achieving high torque density and a wide flux adjustment range. A separated stator structure is adopted to enhance its antidemagnetization capability. To analyze the contributions of AlNiCo and NdFeB to the induced electromotive force (EMF) in the AFHPM-MM, a frozen permeability-based induced EMF calculation method is proposed. Theoretical analysis reveals that the conventional method exhibits substantial errors in calculating the AlNiCo-induced EMF, primarily attributed to its failure to adequately account for the dynamic magnetization characteristic discrepancies of AlNiCo under varying magnetization states. Through the analysis of magnetization variations in AlNiCo during the flux adjustment process under different magnetization states, an improved induced EMF calculation method is proposed. Comparative results indicate that, during the flux enhancement process, the average calculation error of the AlNiCo-induced EMF is reduced from 19.84% to 2.09%, whereas during the flux weakening process, the error is reduced from 3.87% to 1.67%. The proposed method achieves accurate induced EMF calculation for the AFHPM-MM.

In radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has garnered considerable attention owing to its minimal computational demands. However, radar HRRP target recognition still faces numerous challenges, primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes. To alleviate the effect of aspect sensitivity, a novel multi-frame attention network (MFA-Net) comprising a range deformable convolution module (RDCM), multi-frame attention module (MFAM), and global-local Transformer module (GLTM) is proposed. The RDCM is designed to adaptively learn the distance of scattering center migration. Subsequently, the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation. Finally, the GLTM allocates attention between global and local features. The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets, and the recognition rate is enhanced by more than 3% compared to the state-of-the-art methods.

With the recent advances in quantum computing, the key agreement algorithm based on traditional cryptography theory, which is applied to the Internet of Things (IoT) scenario, will no longer be secure due to the possibility of information leakage. In this paper, we propose a anti-quantum dynamic authenticated group key agreement scheme (AQDA-GKA) according to the ring-learning with errors (RLWE) problem, which is suitable for IoT environments. First, the proposed AQDA-GKA scheme can implement a group key agreement against quantum computing attacks by leveraging an RLWE-based key agreement mechanism. Second, this scheme can achieve dynamic node management, ensuring that any node can freely join or exit the current group. Third, we formally prove that the proposed scheme can resist quantum computing attacks as well as collusion attacks. Finally, the performance and security analysis reveals that the proposed AQDA-GKA scheme is secure and effective.