Bridges, especially cable-stayed bridges, play an important role in modern transportation systems. The safety status of bridge cables, as an important component of cable-stayed bridges, determines the health status of the entire bridge. As a non-destructive real-time detection technology, acoustic emission has the advantages of high detection efficiency and low cost. This paper focuses on the issue that a large amount of data are generated during the process of health monitoring of bridge cables. A novel acoustic emission signal segmentation algorithm is proposed with the aim to facilitate the extraction of acoustic emission signal characteristics. The proposed algorithm can save data storage space efficiently. Moreover, it can be adapted to different working conditions according to the adjustment of parameters in order to accurately screen out the target acoustic emission signal. Through the acoustic emission signal acquisition experiments of three bridges, the characteristics of the noise signal in the acquisition process are extracted. A comprehensive analysis of the signal in the time domain, frequency domain and time-frequency domain is carried out. The noise signal filtering parameter thresholds are proposed according to the analysis results.

With the booming development of bridge construction, bridge operation and maintenance have always been major issues to ensure the safety of the community. Affected by the long-term service of bridges and natural factors, the safety and durability of cables can be threatened. Cables are critical stress-bearing elements of large bridges such as cable-stayed bridges. Realizing the health monitoring of bridge cables is the key to ensuring the normal operation of bridges. Acoustic emission (AE) is a dynamic nondestructive testing method that is increasingly used in the local monitoring of bridge cables. In this paper, a testbed is described for generating the acoustic emission signals for signal identification testing with machine learning (ML) models. Owing to the limited number of measured signals being available, an algorithm is proposed to simulate acoustic emission signals for model training. A multi-angle feature extraction method is proposed to extract the acoustic emission signals and construct a comprehensive feature vector to characterize the acoustic emission signals. Seven ML models are trained with the simulated acoustic emission signals. Long short-term memory (LSTM) has been specially applied for deep learning demonstration which requires a large amount of training data. As all machine learning models (including LSTM) provide desired performance, it shows that the proposed approach of simulating acoustic emission signals can be effective.

Medium Access Control (MAC) protocol is one of the key network protocols that ensure Wireless Sensor Networks (WSNs) maintain high performance during communication. MAC protocol design plays an important role in improving the performances of the whole network. First, Wireless Passive Sensor Networks (WPSNs) and MAC protocols are introduced in this paper. Second, some existing MAC protocols are introduced. Sensor MAC (S-MAC) protocol is analyzed and existing improved backoff algorithms are introduced. A new MAC protocol called Improved Sensor MAC (IS-MAC) is then proposed to solve the problem that the contention window (CW) during carrier sense is fixed in S-MAC protocol. IS-MAC protocol is able to adjust CW in terms of network load, so energy consumption can be decreased. Finally, according to the simulation results on NS2, the proposed protocol has better performance in terms of throughput and energy consumption.

Detecting and classifying road damage are crucial for road maintenance. To address the limitations of existing road damage detection methods, including insufficient fine‐grained contextual feature extraction and complex models unsuitable for deployment, this paper proposes a lightweight backbone and neck road damage detection model named LBN‐YOLO. First, the backbone and neck of the original model are improved to be lightweight, and the C2f‐dilation wise residual (C2f‐DWR) module is integrated in the backbone to extract multiscale contextual information. Second, a simplified bidirectional feature pyramid network is employed in the neck structure to optimize the feature fusion network, reducing the number of parameters and simplifying the model complexity. Finally, a dynamic head with self‐attention is introduced to enhance the sensing capability of the detection head, thus improving the precision of detecting occluded small objects. The proposed model’s detection ability is evaluated using a custom road damage dataset. The experimental results demonstrate that our proposed LBN‐YOLO model achieves superior performance compared with the YOLOv8n model, with an increase of 4.1% in mAP@0.5 and a 5.2% enhancement in precision, outperforming other detection models. In addition, the model is evaluated on two public datasets, showing improved detection performance compared with the original model, demonstrating strong generalization capabilities. Code and dataset are available at https://github.com/gzNiuadc/Road-crack-dataset .