查看更多>>摘要：Abstract In vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment (Md32/7)$( {M_d^{32/7}} )$ as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of Md32/7$M_d^{32/7}$ can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.
查看更多>>摘要：Abstract This study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.
查看更多>>摘要：Abstract Particle morphology is a crucial factor influencing the mechanical properties of granular materials particularly in infrastructure construction processes where accurate shape descriptors are essential. Accurately measuring three‐dimensional (3D) morphology has significant theoretical and practical value for exploring the multiscale mechanical properties of civil engineering materials. This study proposes a novel approach using multiview (two‐dimensional [2D]) particle images to efficiently predict 3D morphology, making real‐time aggregate quality analysis feasible. A 3D convolutional neural network (CNN) model is developed, which combines Monte Carlo dropout and attention mechanisms to achieve uncertainty‐evaluated predictions of 3D morphology. The model incorporates a convolutional block attention module, involving a two‐stage attention mechanism with channel attention and spatial attention, to further optimize feature representation and enhance the effectiveness of the attention mechanism. A new dataset comprising 18,000 images of 300 natural gravel and 300 blasted rock fragment particles is used for model training. The prediction accuracy and uncertainty of the proposed model are benchmarked against a range of alternative models including 2D CNN, 3D CNN, and 2D CNN with attention, in particular, to the influence of the number of input multiview particle images on the performance of the models for predicting various morphological parameters is explored. The results indicate that the proposed 3D CNN model with the attention mechanism achieves high prediction accuracy with an error of less than 10%. Whilst it exhibits initially greater uncertainty compared to other models due to its increased complexity, the model shows significant improvement in both accuracy and uncertainty as the number of training images is increased. Finally, residual challenges associated with the prediction of more complex particle angles and irregular shapes are also discussed.
查看更多>>摘要：Abstract Quantifying tiny cracks is crucial for assessing structural conditions. Traditional non‐contact measurement technologies often struggle to accurately measure tiny crack widths, especially in hard‐to‐access areas. To address these challenges, this study introduces an image‐based, handheld parallel laser line‐camera (PLLC) system designed for automated tiny crack localization and width measurement from multiple angles and safe distances. Established by processing parallel laser strips, the camera coordinate system addresses crack positioning and pixel scale distortion challenges typical in non‐perpendicular photography. The determined pixel scale enables accurate width measurement. An improved U‐Net model automatically identifies crack pixels, enhancing detection accuracy. Additionally, the newly developed Equal Area algorithm enables the sub‐pixel width measurement of tiny cracks. Comprehensive laboratory and field testing demonstrates the system's accuracy and feasibility across various conditions. This PLLC system achieves quantitative tiny crack detection in one shot, significantly enhancing the efficiency and utility of on‐site inspections.
查看更多>>摘要：Abstract Particle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle geometry. Noise algorithms are utilized to apply random variations with certain morphological patterns on the surface of the base geometry (e.g., a sphere), thereby generating a variety of particles with morphological patterns ranging from very angular to rounded. In addition, the base geometry can be replaced with other geometries including real particle scans, allowing rapid generation of realistic particles with morphological characteristics of the base geometry. The framework stands out for its simplicity, the wide range of particle morphologies generated, reducing the need for extensive computation and scanning, and provides a new idea for the granular soil behavior simulations.
查看更多>>摘要：Abstract This paper addresses the challenge of real‐time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is established, resulting in optimal trajectories, the sensitive parameters, and the corresponding variation ranges. Second, an RL model is built, and the optimization results obtained offline are used as training samples. The RL‐based method can be applied for continuous digging tasks, which is beneficial for improving the overall efficiency of the autonomous operation of the excavator. Finally, simulation experiments were conducted in four distinct conditions. The results demonstrate that the proposed method effectively accomplishes excavation tasks, with trajectory generation completed within 0.5 s. Comprehensive performance metrics remained below 0.14, and the excavation rate exceeded 92%, surpassing or matching the performance of the optimization‐based method and PINN‐based method. Moreover, the proposed method produced consistently balanced trajectory performance across all sub‐tasks. These results underline the method's effectiveness in achieving real‐time, multi‐objective, and continuous trajectory planning for autonomous excavators.
查看更多>>摘要：Abstract This study presents a scalable multi‐camera system (S‐MCS) for high‐precision displacement measurement and deformation monitoring of long‐span arch bridges during construction. Traditional methods such as robotic total stations (RTS) and single‐camera systems face limitations in dynamic scalability, synchronous multi‐point monitoring, and robustness against environmental disturbances. To address these challenges, the proposed S‐MCS integrates dynamically expandable measuring cameras and dual correcting cameras to compensate for platform ego‐motion. A self‐calibration algorithm and spatiotemporal reference alignment framework are developed to ensure measurement consistency across evolving construction phases. The system was deployed on a 600‐m‐span arch bridge, achieving sub‐millimeter accuracy (root mean square error ≤ 1.09 mm) validated against RTS data. Key innovations include real‐time platform motion compensation, adaptive coverage expansion, and high‐frequency sampling for capturing transient structural responses. Comparative analyses under construction loads, thermal variations, and extreme crosswinds demonstrated the system's superiority in tracking multi‐point displacements, resolving dynamic behaviors and supporting safety assessments. The S‐MCS provides a robust solution for automated, large‐scale structural health monitoring, with potential applications in diverse infrastructure projects requiring adaptive, high‐resolution deformation tracking.
查看更多>>摘要：Abstract Strain is one of the key indicators for structural health monitoring. In this study, we developed a low‐cost microscopic vision‐based real‐time strain sensor using Raspberry Pi (called MISS‐Dym). By strategies for image processing accelerated and the specific running logic, the strain can be outputted at a frequency of more than 30 Hz in real time. The MISS‐Dym integrates multiple functions including real‐time strain calculations, temperature compensation, data storage, and wireless transmission. Comparative experiments were performed with fiber Bragg grating to assess the accuracy of the sensor. In the static experiments, the maximum mean squared error was 1.77 µε, while the maximum relative error was 5.5% in the dynamic experiments. Additionally, a 10‐day monitoring was conducted by MISS‐Dym. The results show that the sensor can effectively capture both the vehicle‐induced and the temperature‐induced strain of the concrete bridge. The MISS‐Dym provides an efficient and low‐cost method for monitoring the dynamic strain responses of concrete structures.
NETL
NSTL
Wiley