Structural Health Monitoring (SHM) systems have been extensively used to ensure the operational safety of long-span bridges. Large-scale bridge structural response and loading data observed from various sensors show obvious big data characteristics. However, serious data island problems, which exist in the conventional SHM solutions, inevitably limit the effects of sensory data analysis and information sharing. A unified bridge SHM semantic representation model is much in demand. By taking the advantages of Semantic Web technologies, this paper presents a novel model, called the Bridge Structure and Health Monitoring (BSHM) ontology, to achieve fine-grained modeling of bridge structures, SHM systems, sensors and sensory data from multiple perspectives. A bridge SHM big data platform is used to demonstrate the usefulness. Several representative data accessing and rule-based reasoning scenarios are employed as to illustrate the advantages of the proposed manner.

Objective: The aim of this paper is to improve the reconstruction accuracy in both position and source region of fluorescence molecular tomography (FMT). Methods: The reconstruction of the FMT is challenging due to its serious ill-posedness and ill-condition. Currently, to obtain the fluorescent sources accurately, more a priori information of the fluorescent sources is utilized and more efficient and practical methods are proposed. In this paper, we took the group sparsity of the fluorescent sources as a new type of priori information in the FMT, and proposed the fused LASSO method (FLM) for FMT. The FLM based on group sparsity prior not only takes advantage of the sparsity of the fluorescent sources, but also utilizes the structure of the sources, thus making the reconstruction results more accuracy and morphologically similar to the sources. To further improve the reconstruction efficiency, we adopt Nesterov's method to solve the FLM. Results: Both heterogeneous numerical simulation experiments and in vivo mouse experiments were carried out to verify the property of the FLM. The results have verified the superiority of the FLM over conventional methods in tumor detection and tumor morphological reconstruction. Furthermore, the in vivo experiments had demonstrated that the FLM has great potential in preclinical application of the FMT. Significance: The reconstruction method based on group sparsity prior has a great potential in the FMT study, it can further improve the reconstruction quality, which has practical significance in preclinical research.

Objective: Fluorescence molecular tomography (FMT) is an important tool for life science, which can noninvasive real-time three-dimensional (3-D) visualization for fluorescence source location. FMT is widely used in tumor research due to its high-sensitive and low cost. However, the reconstruction of FMT is difficult. Although the reconstruction methods of FMT have developed rapidly in recent years, the morphological reconstruction of FMT is still a challenge problem. Thus, the purpose of this study is to realize the morphological reconstruction performance of FMT in glioma research. Methods: In this study, group sparsity was used as a new priori information for FMT. Besides sparsity, group sparsity also takes the group structure of the fluorescent sources, which can maintain the morphological information of the sources. Fused LASSO method (FLM) was proved it can efficiently model the group sparsity prior. Thus, we utilize FLM to reconstruct the morphological information of glioma. Furthermore, to reduce the influence of the high scattering of skull, we modified the FLM for improving the accuracy of morphological reconstruction. Results: Glioma numerical simulation model and in vivo glioma model were established to evaluate the performance of morphological reconstruction of the proposed method. The results demonstrated that the proposed method was efficient to reconstruct the morphological information of glioma. Conclusion: Group sparsity priori can effectively improve the morphological accuracy of FMT reconstruction. Significance: Group sparsity can maintain the morphological information of fluorescent sources effectively, which has great application potential in FMT. The group sparsity based methods can realize the morphological reconstruction, which is of great practical significance in tumor research.

In recent years, the evolution of convolutional neural networks (CNNs) has paved the way for the development of deep learning-based visual inspection techniques to identify damages in concrete bridges. However, the coexistence of diverse damage attributes within a single image complicates the comprehensive interpretation of these images during practical bridge inspections, posing a persistent challenge for existing single classification tasks. To address this issue, we introduce an innovative algorithm that synergizes Graph Convolutional Neural Network (GCN) and MaxViT (Multi-Axis Vision Transformer). Our approach leverages MaxViT to extract image features and subsequently models the co-occurrence correlation of labels. Additionally, we employ a graph convolutional neural network to extract features that capture the correlation between labels. Ultimately, we integrate these image features to enable the multi-label classification of damage images. To evaluate the efficacy of our method, we curated a multi-label damage classification dataset for concrete bridges, consisting of 2098 samples encompassing the four prevalent types of spall, rebar, corrosion, and crack observed during bridge inspections. Experimental results showcase that our proposed method achieves a mean Average Precision (mAP) of 99.37% when applied to real bridge inspection images. In terms of general evaluation metrics, our method attains an accuracy of 98.29%, precision of 96.28%, recall of 97.08%, and F1-score of 96.68%. These findings underscore the superior performance of our method compared to current advanced multi-label classification networks, demonstrating higher accuracy. An interpretable analysis of the classification feature maps generated by our model, utilizing the Grad-CAM algorithm, reveals that the model effectively directs attention to specific regions where damage is located. Consequently, our method holds promising engineering applications and has the potential to significantly enhance the accuracy of support provided for bridge inspection and maintenance endeavors.

Automated damage segmentation for concrete bridges is a fundamental task in infrastructure maintenance, yet existing systems often depend heavily on large annotated datasets, which are costly and time-consuming to produce. This paper presents an innovative framework for concrete bridge damage segmentation, leveraging the Segment Anything Model (SAM) to reduce the reliance on extensive annotated data while enhancing segmentation accuracy and efficiency. Firstly, a SAM-guided mask generation network is introduced, which utilizes the SAM’s segmentation capabilities to generate supplementary supervision labels for damage segmentation. Then, a novel point-prompting strategy, incorporating saliency information, is proposed to refine SAM’s prompts, ensuring accurate mask generation for complex damage patterns. Next, a trainable semantic segmentation network is designed, integrating MambaVision and ResNet as dual backbones to capture multi-level features from concrete bridge damages. To fuse these features effectively, a Hierarchical Attention Fusion (HAF) mechanism is introduced. Finally, a Polarized Self-Attention (PSA) decoder is employed to improve segmentation precision. Experiments on a dataset of 10,000 concrete bridge images with box-level annotations achieved state-of-the-art performance, with an MIoU of 60.13%, PA of 74.02%, and MDice of 75.40%, outperforming existing segmentation models. In summary, this study improves the accuracy of concrete bridge damage segmentation through a series of innovative methods and strategies, and the concrete bridge damage segmentation algorithm opens up new horizons and directions.