A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge

Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.

Acoustic Non-Destructive Testing Technology in Concrete Bridge Inspection and Pile Foundation Detection

This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head

The different imaging conditions of high spatial resolution remote sensing images(HSRRSIs)tend to cause large differences in the background information of bridges from the images,including problems of difficult detection of multiscale bridges,leakage of small bridges and insufficient detection accuracy for their detection.To address these problems,a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper.First,the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network(BiFPN).Then,the convolutional block attention module(CBAM)is fused into the three effective feature layers after feature pyramid network processing.The bridge feature information is effectively extracted from the channel and spatial dimensions.Next,the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously.Finally,the practical effect is evaluated by calculating the average precision(AP).According to the experimental results,the AP of the proposed method is 98.1%,which is improved by 4.1%∼23.5%compared with other models.

The dual cycle bridge detection of piezoresistive triaxial accelerometer based on MEMS technology

A cycle bridge detection method, which uses a piezoresistive triaxial accelerometer, has been described innovatively. This method just uses eight resistors to form a cycle detection bridge, which can detect the signal of the three directions for real time. It breaks the law of the ordinary independent Wheatstone bridge detection method, which uses at least 12 resistors and each four resistors connected as a Wheatstone bridge to detect the output signal from a specific direction. In order to verify the feasibility of this method, the modeling and simulating of the sensor structure have been conducted by ANSYS, then the dual cycle bridge detection method and independent Wheatstone bridge detection method are compared, the result shows that the former method can improve the sensitivity of the sensor effectively. The sensitivity of the x, y-axis used in the former method is two times that of the sensor used in the latter method, and the sensitivity of the z-axis is four times. At the same time, it can also reduce the cross-axis coupling degree of the sensor used in the dual cycle bridge detection method. In addition, a signal amplifier circuit and adder circuit have been provided, Finally, the test result of the ＂eight-beams/mass＂ triaxial accelerometer, which is based on the dual cycle bridge detection method and the related circuits, have been provided. The results of the test and the theoretical analysis are consistent, on the whole.

Development of a novel accelerometer based on an overlay detection bridge

This paper describes the design, simulation, processing and test result of a high sensitivity accelerometer based on the piezoresistive effect which uses an overlay bridge detection method. The structure of this accelerometer is supersymmetric ＂mass-beams＂. This accelerometer has 8 beams, where two varistors are put in the two ends. Four varistors compose a Wheatstone bridge and the output voltages of the 4 Wheatstone bridges have been superimposed as the final output voltage. The sensitivity of the accelerometer can be improved effectively by these clever methods. A simplified mathematical model has been created to analyze the mechanical properties of the sensor, then the finite element modeling and simulation have been used to verify the feasibility of the accelerometer. The results show that the sensitivity of the accelerometer is 1.1381 mV/g, which is about four times larger than that of the single bridge accelerometers and series bridge sensor. The bandwidth is 0-1000 Hz which is equal to that of the single bridge accelerometers and the series bridge sensor. The comparison reveals that the new overlay detection bridge method can improve the sensitivity of the sensor in the same bandwidth. Meanwhile, this method provides an effective method to improve the sensitivity of piezoresistive sensors.