Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. To verify the proposed algorithm, a truss bridge and an arch bridge are analyzed. Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.

Bolted connections have become the most widely used connection method in steel structures. Over the long-term service of the bolts, loosening damage and other defects will inevitably occur due to various factors. To ensure the stability of bolted connections, an efficient and precise method for identifying loosened bolts in a given structure is proposed based on computer vision technology. The main idea of this method is to combine deep learning with image processing techniques to recognize and label the loosening angle from bolt connection images. A rectangular steel plate was taken as the test research object, and three grade 4.8 ordinary bolts were selected for study. The analysis was conducted under two conditions: manual loosening and simulated loosening. The results showed that the method proposed in this article could accurately locate the position of the bolts and identify the loosening angle, with an error value of about ±0.1°, which proves the accuracy and feasibility of this method, meeting the needs of structural health monitoring.

Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.

In the engineering domain, the detection of damage in high-strength bolts is critical for ensuring the safe and reliable operation of equipment. Traditional manual inspection methods are not only inefficient but also susceptible to human error. This paper proposes an automated bolt damage identification method leveraging AIGC (Artificial Intelligence Generated Content) technology and object detection algorithms. Specifically, we introduce the application of AIGC in image generation, focusing on the Stable Diffusion model. Given that the quality of bolt images generated directly by the Stable Diffusion model is suboptimal, we employ the LoRA fine-tuning technique to enhance the model, thereby generating a high-quality dataset of bolt images. This dataset is then used to train the YOLO (You Only Look Once) object detection algorithm, demonstrating significant improvements in both accuracy and recall for bolt damage recognition. Experimental results show that the LoRA fine-tuned Stable Diffusion model significantly enhances the performance of the YOLO algorithm, providing an efficient and accurate solution for automated bolt damage detection. Future work will concentrate on further optimizing the model to improve its robustness and real-time performance, thereby better meeting the demands of practical industrial applications.

For the simple bridge structure, the finite element model established by drawing and elastic mechanics method is accurate. However, when faced with large and complex long-span bridge structures, there are inevitable differences between the finite element model and the physical model, where the model has to be updated. It is problematic that the updating structural matrix cannot be fed back into the existing general finite element calculation software in the traditional structural matrix updating method. In this paper, a parameter-type updating method based on the “Kriging model + swarm intelligence” optimization is proposed. The Kriging model, based on Genetic Algorithm (GA), Bird Mating Optimizer (BMO), and Particle Swarm Optimization algorithm (PSO), is introduced into the finite element model, updating this to correct the design parameters of the finite element model. Firstly, a truss structure was used to verify the effectiveness of the proposed optimization method, and then a cable-stayed bridge was taken as an example. Three methods were used to update the finite element model of the bridge, and the results of the three optimization algorithms were compared and analyzed. The results show that, compared with the other two methods, the GA-based model updating method has the least time due to the small computation. The results of the BMO-based model were time consuming compared to the other two algorithms, and the parameter identification results were better than the GA algorithm. The PSO algorithm-based model updating method to solve the finite element model was repeated, which required a large amount of computation and was more time consuming; however, it had the highest parameter correction accuracy.