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.

In the process of bridge management, large amounts of domain information are accumulated, such as basic attributes, structural defects, technical conditions, etc. However, the valuable information is not fully utilized, resulting in insufficient knowledge service in the field of bridge management. To tackle these problems, this paper proposes a complex knowledge base question answering (C-KBQA) framework for intelligent bridge management based on multi-task learning (MTL) and cross-task constraints (CTC). First, with C-KBQA as the main task, part-of-speech (POS) tagging, topic entity extraction (TEE), and question classification (QC) as auxiliary tasks, an MTL framework is built by sharing encoders and parameters, thereby effectively avoiding the error propagation problem of the pipeline model. Second, cross-task semantic constraints are provided for different subtasks via POS embeddings, entity embeddings, and question-type embeddings. Finally, using template matching, relevant query statements are generated and interaction with the knowledge base is established. The experimental results show that the proposed model outperforms compared mainstream models in terms of TEE and QC on bridge management datasets, and its performance in C-KBQA is outstanding.

As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.

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.

Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.

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.