Structural Damage Identification Based on Information Fusion Techniques

Vibration-based damage identification is a useful tool for structural health monitoring. But, the damage detection results always have uncertainty because of the measurement noise, modeling error and environment changes. In this paper, information fusion based on D-S (Dempster-Shafer) evidence theory and Shannon entropy are employed for decreasing the uncertainty and improving accuracy of damage identification. Regarding that the multiple evidence from different information sources are different importance and not all the evidences are effective for the final decision. The different importance of the evidences is considered by assigning weighting coefficient. Shannon entropy is a measurement of uncertainty. In this paper it is employed to measure the uncertainty of damage identification results. The first step of the procedure is training several artificial neural networks with different input parameters to obtain the damage decisions respectively. Second, weighing coefficients are assigned to neural networks according to the reliability of the neural networks. The Genetic Algorithm is employed to optimize the weighing coefficients. Third, the weighted decisions are assigned to information fusion center. And in fusion center, a selective fusion method is proposed. Numerical studies on the Binzhou Yellow River Highway Bridge are carried out. The results indicate that the method proposed can improve the damage identification accuracy and increase the reliability of damage identification to compare with the method by neural networks alone.

In this study, a two-stage damage detection procedure using a new approximation of modal strain energy (MSE) value is developed to boost the accuracy of structural damage localization in plate-type structures. The MSE is calculated using the center differential method for both intact and damaged states. The new formulas to approximate MSE are developed based on the nine-node isometric quadrilateral element that eliminates boundary conditions’ effect on damage detection results. The modal assurance criteria (MAC) is used as an indicator to choose the mode shapes with the best damage detection results. A global and a local MSE are proposed to enhance the accuracy of detecting the location of structural damage. An aluminum plate is used to verify the effectiveness of the proposed procedure with several damage scenarios. The results show that the new estimation of MSE value combined with MAC and the two-step MSE procedure can improve the accuracy of identifying the damage location compared to the original MSE procedure.KeywordsDamage detectionModal assurance criteriaModal strain energyPlate-type structureStructural health monitoring

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.

The [Formula: see text] regularization is usually used to deal with the problems of under-determinacy and measurement noise for the conventional sensitivity-based model updating damage detection methods. However, the [Formula: see text] regularization technique often provides overly smooth solutions and thus cannot exhibit the sparsity of the structural damage due to the promotion of the 2-norm term on smoothness. In the study, a structural damage detection method is proposed based on an improved modal flexibility sensitivity function and an iterative reweighted [Formula: see text] (IR[Formula: see text] regularization. Specifically, the sensitivity function is established by introducing changes in the mode shapes into the derivative of eigenvalue and can be applied to identify the localized damage more accurately. Additionally, IR[Formula: see text] regularization is proposed to deal with the ill-posed problem of damage detection in a noisy environment. The proposed IR[Formula: see text] regularization is compared with the [Formula: see text] and [Formula: see text] regularizations through a numerical and an experimental examples. The numerical and experimental results indicate that the IR[Formula: see text] regularization can more accurately locate and quantify the single and multiple damages under the noise situation. The maximum identification errors are only 5.16% and 5.67%, respectively. Moreover, compared to the basic modal flexibility sensitivity function, the improved function is more sensitive to the damage. The maximum identification error of the improved function is less than 6%, while the relative errors are significantly larger in the basic function.

Structural health monitoring (SHM) based on long-term safety and efficacy has attracted much attention. The success of SHM methods depends on the information content of the measurements; in addition, there are challenges in processing SHM in terms of the optimal number and location of sensors. In this study, we provide a convenient method for structural damage detection using vibration data from plane steel frames. We apply 1-D convolutional neural networks (1D-CNNs) to present an influential sensor selection analysis to maintain performance and optimize the number and location of sensors. Finding the damage location based on the optimal number and location of sensors will significantly reduce the detection time and the number of installed sensors. The proposed structural damage detection method uses stress analysis to find positions with large displacements, then divides the detection zones, selects highly discriminate sensors, analyzes the influence of sensor combinations within each zone to achieve optimization of sensor number as well as position selection, and establishes CNN models of the overall structure for damage detection of the joints. The proposed method was practically validated on benchmark data from the Qatar University Grandstand Simulator (QUGS). The experimental results show that the sensor demand rate is only 16.67% compared to the previous optimal number and location of sensors with no restrictions on the mounting position. Moreover, only four CNN models are required to predict damage/undamaged for the entire structure (30 connections). The accuracy of damage detection is improved to 96.62% using sensor combinations with influence.

A structural damage detection method by integrating data fusion and random forests was proposed. The original acceleration signals were translated into energy features by wavelet packet decomposition. Then the processed energy features were fused into new energy features by data fusion. This can further enlarge the differences among all types of damages. Finally, random forests as an effective classifier was used to detect the multiclass damage. Numerical study on the benchmark model and an eight-storey steel shear frame structure model was carried out to validate the accuracy of the proposed damage detection method. The experiment results indicate that the damage detection method based on random forests and data fusion can improve damage detection accuracy in comparison with random forests alone, support vector machine alone, and support vector machine and data fusion techniques. Moreover, the proposed method has significantly better stability than several other methods.

The concept of using tunable piezoelectric transducer circuitry to enhance the performance of frequency-shift based damage detection has been recently explored. While previous studies have shown promising features, the advantage of such an approach has not been fully realized due to the limitations in the associated damage identification algorithms. This research aims at advancing the state-of-the-art of this concept, where an iterative eigenvector updating method is developed to improve the accuracy of the damage detection scheme. The basic principle of this method is to iteratively update the eigenvectors of the damaged structure such that the information of the damage-induced eigenvector change can be included in the damage detection formulation without actually measuring the vibration modes. The inductance tuning method is extended to structures with multiple transducers and circuits. Numerical analysis on a plate structure demonstrated the effectiveness and accuracy of the new method in detecting both the location and severity of the structural damage. Nomenclature K = generalized stiffness matrix of the electro-mechanical integrated system e j K = elemental stiffness matrix of the j th element c K = coupling matrix between the mechanical structure and piezoelectric circuit p K = inverse capacitance matrix of the piezoelectric circuit s K = stiffness matrix of the mechanical structure only M = generalized mass matrix of the electro-mechanical integrated system s M = mass matrix of the mechanical structure only q = displacement vector of the mechanical structure Q = electrical charge flow vector in the circuit i ω = i th resonant frequency of the healthy integrated electro-mechanical system () d i ω = i th resonant frequency of the damaged integrated electro-mechanical system i λ = i th eigenvalue of the healthy integrated electro-mechanical system

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

Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique

Purpose The purpose of this paper is to provide an overview of condition monitoring using artificial neural network (ANN) integration as a part of transportation infrastructure systems. Design/methodology/approach This paper will review the concept of ANNs and its core functions for the optimization (to manage the asset in such a way that the condition does not fall below an acceptable minimum condition) of transportation infrastructure systems, in particular, the maintenance processes. In doing so, a specific and factual example of performance and condition measurement for roads will be also instigated. Findings This paper demonstrated that ANN has many advantages if the problems cannot be solved by the clear algorithm. In addition, ANN has the ability to be instructed to handle large data set. There are various intelligent algorithms available and accordingly ANN is not a new concept. However, the ANN’s overall ability to solve complex and interchangeable system problems (such as one, which is found within the transportation infrastructure systems) is its core advantage. Originality/value Although condition monitoring using ANN integration has been researched extensively, this paper provides additional example of integrated ANN for transportation infrastructure systems.

Efficient damage detection technologies are highly demanded in the maintenance of bridge structures. For this purpose, the authors have developed the active Tap-scan damage detection method, which can extract bridge damage from the acceleration of a passing vehicle mounted with a shaker. Although this method can be efficiently implemented in a noisy environment with normal traffic, the damage detection accuracy could be deteriorated by the fluctuation of vehicle velocity, and the portability of the vehicle is impeded by the cumbersome shaker system. To solve these problems, this paper proposes a passive Tap-scan damage detection method, which uses an automatic driving vehicle with specially designed tire treads to scan the bridge. Instead of using the shaker, the tapping force is passively generated by coordinating the vehicle velocity with the periodic pattern of the tire tread. The theoretical basis of this method is firstly established on the analysis of the interaction between a simply supported beam and a passing vehicle with tapping contact forces, as well as the analytical solution of the deflection of a damaged beam subjected to a unit point force. Then, in-house experimental damage detection results of a simply supported aluminium box beam are reported to demonstrate the effectiveness of the proposed method.

Recently, many structural damage detection (SDD) methods have been proposed to monitor the safety of structures. As an important modal parameter, mode shape has been widely used in SDD, and the difference of vectors was adopted based on sensitivity analysis and mode shapes in the existing studies. However, amplitudes of mode shapes in different measured points are relative values. Therefore, the difference of mode shapes will be influenced by their amplitudes, and the SDD results may be inaccurate. Focus on this deficiency, a multi-strategy SDD method is proposed based on the included angle of vectors and sparse regularization in this study. Firstly, inspired by modal assurance criterion (MAC), a relationship between mode shapes and changes in damage coefficients is established based on the included angle of vectors. Then, frequencies are introduced for multi-strategy SDD by a weighted coefficient. Meanwhile, sparse regularization is applied to improve the ill-posedness of the SDD problem. As a result, a novel convex optimization problem is proposed for effective SDD. To evaluate the effectiveness of the proposed method, numerical simulations in a planar truss and experimental studies in a six-story aluminum alloy frame in laboratory are conducted. The identified results indicate that the proposed method can effectively reduce the influence of noises, and it has good ability in locating structural damages and quantifying damage degrees.

Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions

STOCHASTIC SUBSPACE-BASED STRUCTURAL IDENTIFICATION AND DAMAGE DETECTION AND LOCALISATION—APPLICATION TO THE Z24 BRIDGE BENCHMARK

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.