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.

Analysis of Detection and Monitoring Technology in the Construction and Maintenance of Large Bridges

Currently,there is significant attention placed on the construction,management,and maintenance of large service bridges.Within the realm of bridge maintenance management,the utilization of detection and monitoring technology is indispensable.By employing these technologies,we can effectively identify any structural defects within the bridge,promptly uncover unknown risks,proactively establish maintenance strategies,and prevent the rapid deterioration of bridge conditions.This article aims to explore the advantages of applying bridge monitoring and testing technology and to discuss various methods for implementing detection and monitoring technology throughout the construction,management,and maintenance phases of large bridges.Ultimately,this will contribute to ensuring the safe operation of large bridges.

Analysis of Bridge-Bearing Capacity Detection and Evaluation Technology

A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection technology,and the bearing capacity assessment analysis.It is hoped that this analysis can provide a scientific reference for the load-bearing capacity detection and evaluation work in bridge engineering projects,thereby achieving a scientific assessment of the overall load-bearing capacity of the bridge engineering structure.

Applications of Ultrasonic Detection Technology in Bridge Concrete Pile Foundation Detection

In this paper,the application strategy of ultrasonic detection technology in the detection of concrete foundation piles is analyzed using a construction project as an example.It includes a basic overview of the project,an overview of ultrasonic testing technology in bridge concrete pile foundation testing,and an analysis of its practical application in the concrete pile foundation testing of this project.The objective of this analysis is to provide some reference for the application of ultrasonic testing technology and the improvement of the quality of bridge concrete pile foundation testing.

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.

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

Crack detection of reinforced concrete bridge using video image

With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

Dynamic Detection of Reinforced Concrete Bridge Damage by Finite Element Model Updating

The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repaired bridge is carried out. The diagnosis enables us to locate and detect the damage in a reinforced concrete bridge. Thus, developments of analytical predictions have been checked by modal testing techniques. Besides, the FTCS （finite time centered space） scheme is developed to solve the set of equations which can easily handle finite element matrices of a bridge model. It is shown in this study that the method is applied to detect damages as well as existing cracks in real time of a repaired bridge. To check the efficiency of the method, the repaired bridge of OuedOumazer in Algeria has been selected. It is proven that identification methods have been able to detect the exact location of damage areas to be corrected avoiding the inaccuracy from the finite element model for the mass, stiffness and loading.

Comprehensive Detection and Analysis of Defects in Foundation Pile of Bridge

In the process of piling ,there are many various defects in foundation pile of bridge such as mud-bearing,sediment-bearing, isolation, honeycomb, broken piles, and so on, showing physical and mechanical features of low-density and low-intensity. In fact, by using the comprehensive detection of acoustic transmission method, the reflected wave method as well as drill coring sample method, and the rational utilization of engineering geological condition in field, the characteristics, size and location of common defects of foundation pile of bridge can be accurately detected and judged and the integrity of piles and the quality of concrete can be impersonally estimated.comprehensive detecting and analyzing methods on this kind of piles are introduced briefly. The physical characters of defects and basic features of detecting curves and their corresponding relation are emphasized, and causes are analyzed in in detail in this paper.

Finite element analysis on impact of foundation treatment in bridgehead transition section upon bridge piles

In order to decrease relative settlement, foundation treatment plays an extremely important role in bridgehead transition section, especially, the situation of building the bridge piles firstly, and then processing piles. On the basis of engineering practice, the authors analyzed the influence of foundation treatment on bridge piles in bridgehead transition section by finite-element method (FEM). This research has positive significance in predicting displacement of bridge pile, directing construction of foundation treatment, and improving quality of engineering and so forth.

A Method for the Damage Detection of Pile Foundation in High-Pile Wharf Based on A Curvature Mode Deletion Model

As the top of the pile foundation in high-pile wharf is connected to the superstructure and most of the pile bodies are located below the water surface, traditional damage detection methods are greatly limited in their application to pile foundation in service. In the present study, a new method for pile foundation damage detection is developed based on the curve shape of the curvature mode difference(CMD) before and after damage. In the method, the influence at each node on the overall CMD curve shape is analyzed through a data deletion model, statistical characteristic indexes are established to reflect the difference between damaged and undamaged units, and structural damage is accurately detected. The effectiveness and robustness of the method are verified by a finite element model(FEM) of high-pile wharf under different damage conditions and different intensities of Gaussian white noise. The applicability of the method is then experimentally validated by a physical model of high-pile wharf. Both the FEM and the experimental results show that the method is capable of detecting pile foundation damage in noisy curvature mode and has strong application potential.

Model Tests of Pile Defect Detection

The pile, as an important foundation style, is being used in engineering practice. Defects of different types and damages of different degrees easily occur during the process of pile construction. So, dietecting defects of the pile is very important. As so far, there are some difficult problems in pile defect detection. Based on stress wave theory, some of these typical difficult problems were studied through model tests. The analyses of the test results are carried out and some significant results of the low-strain method are obtained, when a pile has a gradually-decreasing crosssection part, the amplitude of the reflective signal originating from the defect is dependent on the decreasing value of the rate of crosssection β. No apparent signal reflected from the necking appeares on the velocity response curve when the value of β is less than about 3. 5 %.

FAULT DETECTION TEST SET FOR TESTABLE REALIZATIONS OF LOGIC FUNCTIONS WITH ESOP EXPRESSIONS

The circuit testable realization and its fault detection for logic functions with ESOP （EXOR-Sum-Of-Products） expressions are studied. First of all, for the testable realization by using XOR gate cascade, a test set with 2n ＋ m ＋ 1 vectors for the detections of AND bridging faults and a test set with 2n ＋ m vectors for the detections of OR bridging faults are presented. Secondly, for the testable realization by using ）（OR gate tree, a test set with 2n ＋ m vectors for the detections of AND bridging faults and a test set with 3n ＋ m ＋ 1 vectors for the detections of OR bridging faults are presented. Finally, a single fault test set with n ＋ 5 vectors for the XOR gate tree realization is presented. Where n is the number of input variables and m is the number of product terms in a logic function.

Effective LiDAR Damage Detection:Comparing Two Detection Algorithms

The health conditions of highway bridges is critical for sustained transportation operations.US federal government mandates that all bridges built with public funds are to be inspected visually every two years. There is a growing consensus that additional rapid and non-intrusive methods for bridge damage evaluation are needed.This paper explores the potential of applying ground-based laser scanners for bridge damage evaluation. LiDAR has the potential of providing high-density,full-field surface static imaging.Hence,it can generate volumetric quantification of concrete corrosion or steel erosion.By recording object surface topology,LiDAR can detect different damages on the bridge structure and differentiate damage types according to the surface flatness and smoothness.To determine the effectiveness of LiDAR damage detection,two damage detection algorithms are presented and compared using scans on actual bridge damages.The results demonstrate and validate LiDAR damage quantification,which can be a powerful tool for bridge condition evaluation.

Monitoring and detection of steel bridge diseases:A review

Fatigue,corrosion,and bolt loosening are the main causes of structural performance degradation and collapse in steel bridges.Accurate monitoring of steel bridge diseases is a basic premise for ensuring high-quality operation and maintenance of steel bridges.In this regard,a summary and analysis were conducted on the classification of steel bridge diseases,monitoring and detection methods,application statuses,and major difficulties.The main causes,research status,and development trends of steel bridge diseases are discussed.The results showed that,for fatigue crack problems,fatigue crack initiation has a small scale,high difficulty in monitoring and detection,few methods,and low accuracy.As the cracks grow,the difficulty of monitoring and detection decreases,the number of methods increases,and the accuracy improves.Fatigue crack monitoring and detection are affected by the environmental and vehicular loads.Superficial corrosion features are evident in steel bridges,and corrosion identification methods and technologies are rapidly developing.Monitoring and detecting corrosion in concealed areas is difficult and requires further improvements in monitoring and detection technologies and their accuracy.Monitoring and detection methods and supporting equipment for bolt loosening in steel bridges are rapidly developing.The development of intelligent monitoring and detection technologies and supporting equipment is an important research topic that urgently needs to be addressed for the full-lifecycle operation and maintenance of steel bridges and the sustainable development of bridge engineering.Developing new intelligent sensing components based on high-performance materials and sensing element design theory to improve the monitoring and detection perception ability is an important development direction for steel bridge monitoring and detection.Research on intelligent monitoring and detection technologies,standardized indicators,and related topics based on intelligent operations and maintenance provide great support for the development of steel-bridge disease monitoring and detection.

Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images

Bridges are an important part of railway infrastructure and need regular inspection and maintenance.Using unmanned aerial vehicle(UAV)technology to inspect railway infrastructure is an active research issue.However,due to the large size of UAV images,flight distance,and height changes,the object scale changes dramatically.At the same time,the elements of interest in railway bridges,such as bolts and corrosion,are small and dense objects,and the sample data set is seriously unbalanced,posing great challenges to the accurate detection of defects.In this paper,an adaptive cropping shallow attention network(ACSANet)is proposed,which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited samples.To enhance the accuracy and generalization of the model,the shallow attention network model integrates a coordinate attention(CA)mechanism module and an alpha intersection over union(α-IOU)loss function,and then carries out defect detection on the bolts,steel surfaces,and railings of railway bridges.The test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP(an evaluation index)and missing bolt mAP by 5%and 30%,respectively.Also,compared with the YOLOv5s model that adopts the common cropping strategy,the total mAP and missing bolt mAP are improved by 10%and 60%,respectively.Compared with the YOLOv5s model without any cropping strategy,the total mAP and missing bolt mAP are improved by 40%and 67%,respectively.

Damage Detection for Simply Supported Bridge with Bending Fuzzy Stiffness Consideration

The benchmark of a simply supported beam with damage and bending fuzzy stiffness consideration is established to be utilized for damage detection. The explicit expression describing the Rotational Angle Influence Lines(RAIL) of the arbitrary section in the benchmark is presented as the nonlinear relation between the moving load and the RAIL appeared, when the moving load is located on the damage area. The damage detection method is derived based on the Difference of the RAIL Curvature(DRAIL-C) prior to and following arbitrarily section damage in a simply supported beam with bending fuzzy stiffness consideration. The results demonstrate that the damage position can be located by the DRAIL-C graph and the damage extent can be calculated by the DRAIL-C curve peak. The simply supported box girder as a one-dimensional model and the simply supported truss bridge as a three-dimensional model with the bending fuzzy stiffness are simulated for the validity of the proposed method to be verified. The measuring point position and noise intensity effects are discussed in the simply supported box girder example. This paper provides a new consideration and technique for the damage detection of a simply supported bridge with bending fuzzy stiffness consideration.