Wire breakage is a major factor in the failure of prestressed concrete cylinder pipes (PCCP). In the presented work, an automatic monitoring approach of broken wires in PCCP using fiber-optic distributed acoustic sensors (DAS) is investigated. The study designs a 1:1 prototype wire break monitoring experiment using a DN4000 mm PCCP buried underground in a simulated test environment. The test combines the collected wire break signals with the previously collected noise signals in the operating pipe and transforms them into a spectrogram as the wire break signal dataset. A deep learning-based target detection algorithm is developed to detect the occurrence of wire break events by extracting the spectrogram image features of wire break signals in the dataset. The results show that the recall, precision, F1 score, and false detection rate of the pruned model reach 100%, 100%, 1, and 0%, respectively; the video detection frame rate reaches 35 fps and the model size is only 732 KB. It can be seen that this method greatly simplifies the model without loss of precision, providing an effective method for the identification of PCCP wire break signals, while the lightweight model is more conducive to the embedded deployment of a PCCP wire break monitoring system.

The low energy conversion efficiency of electromagnetic acoustic transducers (EMATs) is a critical issue in nondestructive testing applications. To overcome this shortcoming, a butterfly coil EMAT was developed and optimized by numerical simulation based on a 2−D finite element model. First, the effect of the structural parameters of the butterfly coil EMAT was investigated by orthogonal test theory. Then, a modified butterfly coil EMAT was designed that consists of three−square permanent magnets with opposite polarity (TSPM−OP) to enhance the signal amplitude. Finally, the signal amplitude obtained from the three types of EMATs, that is, the traditional EMAT, the EMAT optimized by orthogonal test theory, and the modified EMAT with TSPM−OP, were analyzed and compared. The results show that the signal amplitude achieved by the modified butterfly coil EMAT with TSPM−OP can be increased by 4.97 times compared to the traditional butterfly coil EMAT.

The energy conversion of electromagnetic acoustic transducers (EMATs) is typically lower, which seriously restricts the application of EMATs in the field of non-destructive testing and evaluation. In this work, parameters of surface wave EMATs, including structural parameters and electrical parameters, are investigated using the orthogonal test method to improve the transducer’s energy conversion efficiency. Based on the established finite element 2-D model of EMATs, the amplitude of the displacement components at the observation point of a plate is the optimization objective to be maximized with five parameters pertaining to the magnets, meander-line coils, and excitation signal as design variables. Results show that the signal amplitude of EMATs is 3.48 times on in-plane and 3.49 times on out-of-plane, respectively, compared with the original model. Furthermore, a new material (amorphous nanocrystalline material of type 1K107) is applied to optimize the magnetic circuit of EMATs and enhance the eddy current in an aluminum plate to increase the signal amplitude. Finally, the signal amplitudes obtained from the three types of models, that is, the original one, the optimization one after an orthogonal test, and the optimization one with the addition of magnetic concentrators, are analyzed and compared, indicating that the signal amplitude, compared with the original one, is 6.02 times on in-plane and 6.20 times on out-of-plane, respectively.

This paper presents an obstacle-avoidance trajectory tracking method based on a nonlinear model prediction, with a dynamic environment considered in the trajectory tracking of nonholonomic mobile robots for obstacle avoidance. In this method, collision avoidance is embedded into the trajectory tracking control problem as a nonlinear constraint of the position state, which changes with time to solve the obstacle-avoidance problem in dynamic environments. The CasADi toolkit was used in MATLAB to generate a real-time, efficient C++ code with inequality constraints to avoid collisions. Trajectory tracking and obstacle avoidance in dynamic and static environments are trialed using MATLAB and CasADi simulations, and the effectiveness of the proposed control algorithm is verified.

The failure accidents of prestressed concrete cylinder pipe (PCCP) seriously affect the economic feasibility of the construction site. The traditional method of needing to stop construction for pipe inspection is time-consuming and laborious. This paper studies the PCCP broken wire identification algorithm based on deep learning. A PCCP wire-breaking test platform was built; the Distributed Fiber Acoustic Sensing Monitoring System (DAS) monitors wire-breakage events in DN4000mm PCCPs buried underground. The collected broken wire signal creates a time-frequency spectrum diagram dataset of the simulated broken wire signal through continuous wavelet transform (CWT). Considering the location of equipment limitations, based on the YOLOv5 algorithm, a lightweight algorithm, YOLOv5-Break is proposed for broken wire monitoring. Firstly, MobileNetV3 is used to replace the YOLOv5 network backbone, and Dynamic Conv is used to replace Conv in C3 to reduce redundant computation and memory access; the coordinate attention mechanism is integrated into the C3 module to make the algorithm pay more attention to location information; at the same time, CIOU is replaced by Focal_EIoU to make the algorithm pay more attention to high-quality samples and balance the uneven problem of complex and easy examples. The YOLOv5-Break algorithm achieves a mAP of 97.72% on the self-built broken wire dataset, outperforming YOLOv8, YOLOv9, and YOLOv10. Notably, YOLOv5-Break reduces the model weight to 7.74 MB, 46.25% smaller than YOLOv5 and significantly lighter than YOLOv8s and YOLOv9s. With a computational cost of 8.3 GFLOPs, YOLOv5-Break is 71.0% and 78.5% more efficient than YOLOv8s and YOLOv9s. It can be seen that the lightweight algorithm YOLOv5-Break proposed in this article simplifies the algorithm without losing accuracy. Moreover, the lightweight algorithm does not require high hardware computing power and can be better arranged in the PCCP broken wire monitoring system.