A Method for Damage Detection Based on Correlation Characteristic of Acceleration Response

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

In vibration based modal identification of railways bridges, if the mass of the train is not negligible as compared to that of the bridge, the modal parameters of the train-bridge system becomes time-varying, making modal identification challenging. In this study, a method is developed to identify such time-varying modes of a train-bridge system using moving train induced vibration data. The method utilizes the continuous but gradual variation in the modal parameters to perform a window-wise identification of these parameters. In each time window, an auto-regressive model of the system is first identified, which is then used to identify the modes in that window using the Eigensystem Realization Algorithm. A damage index, based on damage locating vectors, to detect damage location using such identified time-varying modes is also discussed. The proposed time-varying modal identification and damage detection methods are illustrated using laboratory experimental data from a two span continuous steel bridge, as well as a damaged version of the bridge, excited using a moving laboratory scale model train.KeywordsTime varying systemAuto regressive modelEigensystem realization algorithmDamage locating vectorMoving train laboratory experiment

Two major challenges associated with a vibration-based damage detection method using changes in natural frequencies are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistic function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using the Levenberg-Marquardt method is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. It is applied to various engineering structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation, and the locations and extent of damage can be successfully detected in experimental damage detection.

Large scale tests with progressive damage on a prestressed concrete highway bridge have been performed to investigate the sensitivity of several damage detection, localization, and quantification methods based on modal parameters. To investigate the quality of modal parameters, the data set of one damage step was analyzed by several output-only identification techniques. Although the bridge was severely cracked, natural frequencies as well as mode shapes display only minor changes. However, the relative changes of mode shapes are larger than those observed for natural frequencies. A novel damage indicator, called mode shape area index, based on changes of mode shapes, has been developed and found as the most sensitive damage detection approach. Damage detection or localization via changes of the flexibility matrix performed better than natural frequencies or mode shapes alone. The application of the direct stiffness calculation and a sensitivity-based model update technique showed results having a high level of ambiguity about the location and quantification of damage also at the highest damage level. Evaluating the information collected in this study the test results indicate that an early stage damage identification in prestressed concrete bridges is hardly possible because of the nearly complete recovery of stiffness after closing of cracks in prestressed concrete and the effect of environmental parameters on modal data.

Influence line (IL)–based damage indices for long-span bridges are investigated in this study. Their effectiveness is experimentally validated for the first time through the scaled physical model of the Tsing Ma Bridge (TMB). First, the IL mechanism for damage detection and its corresponding damage indices are briefly introduced. Subsequently, the scaled TMB model instrumented with different types of sensors, including displacement sensors, strain gauges, and accelerometers, is introduced. The IL characteristics of different bridge components are compared. Two different damage cases with single- and double-damage locations at the bottom chord were tested. In the single-damage case, different ILs extracted from the nearby components were used for damage identification. These ILs can successfully locate damage visually. The strain IL (SIL) is more sensitive to local damage than deflection IL, but its detection performance degrades rapidly with the increasing distance between sensor and force locations. In the double-damage case, the SIL extracted from a single sensor cannot identify both damage cases because of the limited detectable range of each SIL; therefore, using multiple sensor information becomes necessary. For comparison, the modal parameters were also employed for damage detection. These experimental results validated the merits of the IL–based methods proposed for long-span bridges, indicating that IL–based damage indices are good indicators of local damage detection in long-span bridges. This finding contributes to the development of real-time techniques for damage localization in long-span bridges equipped with a structural health monitoring system.

This paper presents an experimental study on the detection and localization of damage in a steel plate using vibration-based methods. Dynamic impulse tests were conducted on both undamaged and damaged plate to determine changes in modal parameters, such as eigenfrequencies and mode shapes. Several damage detection methods, including the Modal Assurance Criterion (MAC), Coordinate Modal Assurance Criterion (COMAC), Mode Shape Curvature (MSC), Mode Shape Curvature Square (MSCS), and Damage Index (DI), were applied. The results showed that while MAC and COMAC effectively detected damage, they did not provide information about the location. MSC and MSCS indices demonstrated improved damage localization, with the MSCS being particularly effective. The DI index provided the clearest and most accurate representation of the damage location. These findings highlight the potential of these methods for non-destructive testing and structural health monitoring.

Considering the issue of misjudgment in railway bridge damage identification, a method combining the step-by-step damage detection method with the statistical pattern recognition is proposed to detect the structural damage of a railway continuous girder bridge. The whole process of damage identification is divided into three identification sub-steps, namely, damage early warning, damage location, and damage extent identification. The multi-class pattern classification algorithm of C-support vector machine and the regression algorithm of ε-support vector machine are engaged to identify the damage location and damage extent, respectively. For verifying the proposed method, both of the proposed method and the optimization method are used to deal with the measured data obtained from a specific railway continuous girder model bridge. The results show that the proposed method can not only identify the damage location correctly, but also obtain the damage extent which is consistent with the experimental results accurately. By uncoupling finite element analysis and damage identification, normalizing the index, and seeking the separation hyper plane with maximum margin, the proposed method has more favorable advantages in generalization and anti-noise. As a result, it has the ability to identify the damage location and extent, and can be applied to the damage identification in real bridge structures.

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

One of the important goals of structural health monitoring is damage detection. Although many methods have been proposed to detect the existence of structural damage, relatively few studies are found on higher-level damage diagnosis such as identification of the location and extent of damage. In this paper, multiple substructural damage identification models based on regression between internal responses and boundary responses of individual beam elements in either plane or three-dimensional space are derived. Three damage indexes are defined from regression model characteristics, and two change-point analysis methods are adopted to capture changes in damage index sequences which are extracted from structural monitoring data sets from healthy and unknown states. Possible damage locations are identified as where the most significant changes in the damage indexes occur, and a voting scheme is used to synthesize the results from different algorithms. This damage detection approach is straightforward and efficient, with the regression coefficients directly related to the structural stiffness properties. The numerical and experimental application results show that the method successfully identifies and locates structural change in most of the cases.

Effect of response type and excitation frequency range on the structural damage detection method using correlation functions of vibration responses

In this paper, a two-stage damage detection method is proposed to integrate unsupervised and supervised approaches, leveraging their complementary strengths to enhance the method’s capability in damage identification. In the first stage, an unsupervised damage detection method based on convolutional autoencoders (CAEs) is introduced to preliminarily determine whether a signal is abnormal. It is trained solely on normal signals and subsequently reconstructs future signals. In addition to the reconstruction error analysis/indicator, a novel damage indicator referred to as the signal-to-reconstruction error ratio (SRER) is introduced to evaluate the reconstruction accuracy and identify potential anomalies. By monitoring the evolution of these two indicators over time, the method achieves 100% accuracy in detecting structural anomalies. In the second stage, to improve efficiency and mitigate the impact of noisy signals on detection results, two damage indicators are used as inputs for supervised deep learning models, achieving over 90% accuracy in damage localization with these two damage-sensitive features. Furthermore, using these two features as inputs improved accuracy by 2–30% compared to conventional vibration inputs. The proposed method and novel damage indicators demonstrate the ability to rapidly detect and accurately locate structural damage, showcasing significant potential for practical engineering applications.

BP neural network is a multilayer feedforward network for training according to the error back-propagation algorithm , its main advantage is the strong non-linear mapping ability , but the training of BP neural network is easy to fall into local minima, and slow convergence speed. In this paper, through the Levenberg-Marquardt algorithm to improve the BP neural network , using LM algorithm terative results dynamically adjust the convergence direction of neural network,so that each iteration error are decreased, and the convergence speed is fast.Through example analysis, show that the improved BP neural network utility and effectiveness in enterprise performance level evaluation.

Damage detection in beams using vibratory power estimated from the measured accelerations

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

In recent years, thanks to the development of computer vision technology and digital image technology, traditional agriculture and information technology have been deeply integrated. This study uses image recognition and bp neural network technology to identify the types of fruits and selects apples as objects for quality evaluation. First, 150 images of apples, bananas, peaches, avocados, and cherries were selected from the fruit database Fruit360 after grayscale processing as a data set. After the images were grayscaled, image denoising, edge detection, and other feature processing in MATLAB, The classification of five kinds of fruits has been successfully realized, and the correct rate of evaluation reached 94.8%. Then divide the data sets for apples of different varieties and qualities, and input them into the bp neural network for training. After testing, the 6-layer bp network has the highest accuracy rate, and can effectively classify and score apple images.