Damage Identification of a Steel Plate Using Vibration Methods

Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection

Use of modal testing to identify damage on steel members

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

Structural health monitoring (SHM) is essential for ensuring the durability and safety of steel bridges, which are considered as critical part of modern infrastructure. Ensuring the integrity of bridges is vital for maintaining their functionality and safety. Considerable research has been conducted on predicting damage location in multi-span steel bridges, while relatively limited attention has been given to the identification of damage in single-span steel bridges, and there exists a notable gap in the literature regarding the quantification of damage based on severity. This study presents a novel approach combining Modal Strain Energy-based damage indices with extensively trained data-driven Artificial Neural Networks (ANNs) to achieve highly accurate damage location and quantify this damage in terms of severity in single-span steel bridges. The research integrates a finite element (FE) model as the case study of the Alamosa Canyon Bridge in Sierra County, New Mexico. The validated FE model was used to compute damage indices based on the first two bending mode shapes. The damage index (DI) of each mode for different damage scenarios simulated on the FE model is then combined using the absolute value method to pinpoint damage locations. ANNs are employed to predict severity based on these damage indices, leveraging a comprehensive dataset generated through cubic spline interpolations and FE simulations. The DI was used as an input parameter for the ANNs to estimate damage. This methodology highlights the reduced computational effort using a streamlined method for structural health monitoring that maintains high accuracy in severity prediction and damage detection. This study enhances the infrastructure safety and maintenance strategies by offering a practical framework for steel bridges.

A structure's health or level of damage can be monitored by identifying changes in structural or modal parameters. This research directly identifies changes in structural stiffness due to modelling error or damage for a post-tensioned pre-cast reinforced concrete frame building with rocking beam column connections and added damping and stiffness (ADAS) elements. A structural health monitoring (SHM) method based on adaptive least mean squares (LMS) filtering theory is presented that identifies changes from a simple baseline model of the structure. This method is able to track changes in the stiffness matrix, identifying when the building is (1) rocking, (2) moving in a hybrid rocking–elastic regime, or (3) responding linearly. Results are compared for two different LMS-based SHM methods using an L2 error norm metric. In addition, two baseline models of the structure, one using tangential stiffness and the second a more accurate bi-linear stiffness model, are employed. The impact of baseline model complexity is then delineated. The LMS-based methods are able to track the non-linearity of the system to within 15% using this metric, with the error due primarily to filter convergence rates as the structural response changes regimes while undergoing the El Centro ground motion record. The use of a bi-linear baseline model for the SHM problem is shown to result in error metrics that are at least 50% lower than those for the tangential baseline model. Errors of 5–15% with this L2 error norm are fairly stringent compared to the greater than 2 × changes in stiffness undergone by the structure, however, in practice the usefulness of the results is dependent on the resolution required by the user. The impact of sampling rate is shown to be negligible over the range of 200–1000Hz, along with the choice of LMS-based SHM method. The choice of baseline model and its level of knowledge about the actual structure is seen to be the dominant factor in achieving good results. The methods presented require 2.8–14.0 Mcycles of computation and therefore could easily be implemented in real time. Copyright © 2005 John Wiley & Sons, Ltd.

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.

Many composite materials and structures are susceptible to defects, which can significantly reduce the strength of structures and may grow to failure. To avoid the catastrophic failure of structures, development of a reliable method of structural health monitoring is one of the most important keys in maintaining the integrity and safety of structures. Dynamic response-based damage detection offers a simple procedure as an alternative to the conventional nondestructive evaluation techniques. However, this technique depends on the quality of measured data for its identification accuracy. In this article, experimental aspects of dynamic response-based damage detection technique on carbon/ epoxy composites are addressed. Smart piezoelectric materials are used as sensors or actuators to acquire the curvature modes of structures. These materials are surface-bonded to the beams. An impulse hammer is used as an actuating source as well. Four types of damage detection algorithms are evaluated for several possible damage configurations with two different excitation sources. The quality of damage identification with the four different detection algorithms is discussed. These experimental damage identification techniques using curvature modes and piezoelectric materials can be effectively used in damage detection and health monitoring of composite structures.

This paper addresses the problem of damage detection in suspension bridge hangers, with an emphasis on the modal flexibility method. It aims at evaluating the capability and the accuracy of the modal flexibility method to detect and locate single and multiple damages in suspension bridge hangers, with different level of severity and various locations. The study is conducted numerically and experimentally on a laboratory suspension bridge mock-up. First, the covariance-driven stochastic subspace identification is used to extract the modal parameters of the bridge from experimental data, using only output measurements data from ambient vibration. Then, the method is demonstrated for several damage scenarios and compared against other classical methods, such as: Coordinate Modal Assurance Criterion (COMAC), Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC) and Modal Strain Energy (MSE). The paper demonstrates the relative merits and shortcomings of these methods which play a significant role in the damage detection of suspension bridges.

In this paper, modal characteristics of self-compacting concrete (SCC) beams were studied through experiment. 3 self-compacting concrete beams were gradually damaged and then subjected to vibration test in free-free boundary conditions after each load step. From analysis, eigenfrequencies could indicate the existence of damage but it could not represent the damage locations. Modal Assurance Criterion (MAC) is lesser reliable to indicate crack damage compare to eigenfrequencies. Next to these, capacity for damage detection and localization of Coordinate Modal Assurance Criterion (COMAC) and the flexibility matrix method are also examined and compared. This paper introduces in detail the dynamic properties and damage localization methods in SCC, which provide basal databases of damage diagnosis for SCC structures and promote the popularization of SCC in civil infrastructures.

In civil engineering, health monitoring of structure have been treated as an important subject. Health monitoring by using experimental measurements such as strain, acceleration, velocity, displacement, rotation and other parameters has been used for detecting changes, which may indicate damage or degradation. In recent, the applications of health monitoring of large structure have been facilitated by the developments in measurement, communications and computational technology gradually. Especially, large civil structures as bridges are damaged by fatigue loads or unexpected load such as earthquakes and finally reach to failure. Thus, a structural health monitoring and damage detection methods have been developed and attracted a great deal of public attention in recent decades. Actually many research efforts have been conducted to develop a health monitoring and damage detection techniques of existing civil structures. However, these methods are needed to improve to apply to a real civil structure. The purpose of this study is to develop a practical instrument to identify dynamic parameters of real civil structural systems.

Damage within a structure refers to changes in both its geometric and material characteristics, resulting in a drop in the stiffness that impacts the structure’s performance adversely. This decrease in stiffness causes alterations in modal parameters, including natural frequencies and mode shapes. Utilizing modal analysis allows for the extraction of modal frequencies and mode shapes, facilitating the analysis of mode shape curvature to detect structural damage. In recent years, artificial neural networks (ANNs) have achieved significant application, mainly for their exceptional capability in pattern recognition, which proves invaluable for identifying structural damage. This article proposes a novel method based on mode shape curvature and ANNs for detecting damage in beam-like structures. Experimental study is conducted to analysis damaged and undamaged structural modal behaviours. A feed-forward neural network with two hidden layers, trained on damage indices from mode shape data, is used to accurately pinpoint damage locations within the structure. The proposed approach for damage detection is validated and proves its ability to precisely pinpoint the location of damage. The results of this study demonstrate that ANNs trained with modal curvatures hold significant promise for identifying structural damage, enabling early detection in beam-like structures and contributing to ensuring their safe operation.

This paper presents an overview of current research and development in the field of sensor placement methods in structural health monitoring. Due to the limitations of equipment facilities and cost, the number of sensors to be installed in a structure is relatively few. In many cases, three are always degrees of freedoms in a structure are not easily accessible, or eventually inaccessible. Moreover, actual structures are all continuous and have essentially infinite degrees of freedoms, therefore, it is impossible to instrument on all the degrees of freedoms. The methods of sensor placement are mainly dealing with accelerometers for global dynamic testing, or structural health monitoring. Three major concerns are discussed, the necessary number of sensors to be installed, where to deploy these sensors, and how to evaluate the effectiveness of these deployed sensors. Finally, a new loading dependent sensor placement method is proposed, and verified by the experimental data of a simple cantilever beam structure.

This study presents a modal-based damage detection technique for framed structures. A framed structure is tested using a vibration-based method and the mode shapes corresponding to undamaged and damaged states of the structure are obtained. The damage to the framed structure is induced by reducing the stiffness of columns at a particular storey and tested using the shaking table. The corresponding finite element (FE) model was verified for its natural frequency with the experimental model. Moreover, a series of studies were performed by concentrating the damage to a smaller scale by including spot damage on the framed structure and varying the damage location and intensity. The model was analysed using finite element software for these tests and mode shape slope differences and mode shape curvature differences were generated. Derivatives of mode shape slopes were plotted and correlated with the damage location. Mode shape slope is discontinuous and becomes a maximum value close to the damaged location. Mode shape curvatures also show a maximum difference near the damaged location. Finite element model, as well as the experimental investigation, shows the competence of the proposed approach in structural damage identification.

This study tackles the challenge of operational damage identification on wind turbine blades and proposes a novel framework for damage detection and localization. A vibration-based scheme is proposed, which tracks the variability of the mode shape curvatures (MSCs) of the blade along its plane direction. The method consists of a training and a diagnostics stage. In the former, MSC information for a number of predefined modes is extracted over varying operational conditions in the healthy state of the blade and, via the implementation of the principal component analysis (PCA), a statistical characterization of each blade’s node is estimated. Then, during the diagnostics stage, the MSCs are assembled and the same PCA mapping is enforced. A corresponding damage index is established, in order to detect and localize damage, if it exists. In both stages, MSC extraction is based on the successful estimation of vector autoregressive moving average (VARX) models that rely on pressure excitation and distributed strain measurements.

Damage detection in structural elements using curvature mode shape technique has become a research focus of increasing interest during the last few years. A noticeable deficiency of curvature mode shape, however, is its susceptibility to measurement noise, easily impairing its advantage of sensitivity to damage. To overcome this drawback, a nonlinear operator called Teager Energy Operator (TEO) is incorporated. The efficacy of TEO is analytically verified through modal curvatures in a steel reinforced concrete cantilever beam with an induced damage (stiffness loss) along its length. The applicability of the proposed curvature mode shape technique is experimentally validated for detecting simulated damage (mass attached) in an aluminum plate from mode shapes acquired by a non-contact Scanning Laser Doppler Vibrometer (SLDV). Normal responses (out of plane flexure response) are measured with SLDV. The excitation is given with the help of a data-physics exciter based on the frequencies obtained from the free vibration tests using a roving hammer attached with a forced transducer. The proposed algorithm is to compute a robust nonlinear operator - TEO using the attained curvature, dynamic rotation and displacement mode shapes. This algorithm has been successfully implemented and tested for detecting damages on beam and plate elements, using finite element simulations as well as laboratory experiments. It is anticipated that the suggested approach facilitates damage localization in a comparatively fast and accurate manner.