Computer-Aided Civil and Infrastructure Engineering最新文献

The cover image is based on the Research Article 365-day sectional work zone schedule optimization for road networks considering economies of scale and user cost by Yuto Nakazato and Daijiro Mizutani et al., https://doi.org/10.1111/mice.13273.

The cover image is based on the Research Article Intention-aware robot motion planning for safe worker-robot collaboration by Yizhi Liu and Houtan Jebelli et al., https://doi.org/10.1111/mice.13129.

The lattice discrete particle model (LDPM) has been proven to be one of the most appealing computational tools to simulate fracture in quasi-brittle materials. Despite tremendous advancements in the definition and implementation of the method, solution strategies are still limited to dynamic algorithms, resulting in prohibitive computational costs and challenges related to solution accuracy for quasi-static conditions. This study presents a novel static solver for LDPM, introducing fundamental innovation: (1) LDPM constitutive laws are modified to provide continuous response through all possible strain/stress states; (2) an adaptive arc-length method is proposed in combination with a criterion to select the sign of the iterative load factor; (3) an adaptive limit-unloading–reloading path switch algorithm is proposed to restrict oscillations in the global stiffness matrix. Extensive validation of the proposed approach is presented. Numerical results demonstrate that the static solver exhibits satisfactory convergence rates, significantly outperforming available dynamic solutions in computational efficiency.

The phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle weighing systems can only meet some of the requirements of different scenarios. The development of artificial intelligence technologies, especially deep learning, has greatly enhanced the accuracy and efficiency of computer vision. To this end, the paper proposes a method using computer vision and deep learning for the non-contact identification of overweight vehicles. By constructing two deep learning models and combining them with the vehicle vibration model and relevant specifications, the weight and maximum allowable weight of the vehicle are obtained to make a comparison for determining overweight. Experimental verification was performed using a two-axle vehicle as an illustrative example, and the results demonstrate that the proposed method exhibits excellent feasibility and effectiveness. It shows significant potential in real-world scenarios, laying a research foundation for practical engineering applications. Additionally, it provides a reference for the governance and decision-making of overweight issues for relevant authorities.

Physics-Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics-Informed Neural Operator Solver (PINOS) to achieve accurate and fast simulations without any required data set. The training of PINOS adopts a weak form based on the principle of least work for static simulations and a strong form for dynamic systems in solid mechanics. Results from numerical examples indicated that PINOS is capable of approximating solutions notably faster than the benchmarks of PINNs in both static an dynamic systems. The comparisons also showed that PINOS reached a convergence speed of over 20 times faster than finite element software in two-dimensional and three-dimensional static problems. Furthermore, this study examined the zero-shot super-resolution capability by developing Super-Resolution PINOS (SR-PINOS) that was trained on a coarse mesh and validated on fine mesh. The numerical results demonstrate the great performance of the model to obtain accurate solutions with a speed up, suggesting effectiveness in increasing sampling points and scaling a simulation. This study also discusses the differentiation methods of PINOS and SR-PINOS and suggests potential implementations related to forward applications for promising machine learning methods for structural designs and optimization.

Accurate wind pressure analysis on high-rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high-rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real-world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real-world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high-rise buildings, paving the way for advancements in digital twins, real-time monitoring, and risk assessment of wind loads.

The meso-structure of pervious concrete significantly influences its overall performance. Accurately identifying the meso-structure of pervious concrete is imperative for optimizing the design of pervious concrete, considering its mechanical properties and functionality. Therefore, to address the difficulty of recognizing the meso-structures of pervious concrete, a method utilizing deep learning image semantic segmentation techniques is proposed in this study. First, based on the classical deep learning model, three models, namely, Res-UNet, ED-SegNet, and G-ENet, are proposed for recognizing pervious concrete meso-structure using deep learning image semantic segmentation techniques. These models introduce a residual module, a hybrid loss function, and a differential recognition branching structure to enhance the ability to recognize detailed information within pervious concrete meso-structure and small targets. Second, the respective recognition performances of these methods on the meso-structure of pervious concrete were thoroughly analyzed by experiment. The results indicate that the proposed three recognition methods for recognizing the meso-structure of permeable concrete outperform conventional techniques not only in terms of efficiency but also in recognition accuracy and the ability to distinguish and identify aggregates, pores, and cement binders. In terms of comprehensive recognition effectiveness, the Res-UNet model outperforms, followed by ED-SegNet and G-ENet. Furthermore, the computational efficiency of these three recognition methods meets the requirements of engineering applications.