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

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.

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

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.

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.

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

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.

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

Efficient wind energy harvesting becomes more and more important as a consequence of the increasing interest in renewable energy in the European Union [1]. This leads to growing sizes of wind turbines (WTs), and with it, larger WT blades (WTBs). The structural designs of these WTBs are created to optimize the potential energy output, where low mass is a key requirement. However, high flexibilities and lower buckling capacities are further results of these developments [2], thus certain damage scenarios become significant. Intelligently designed structural health monitoring (SHM) systems can help to reduce the associated operations and maintenance costs. Even though, several techniques are already developed for structural damage detection (SDD) in WTBs, the majority of these methods is not suitable for inservice measurements. This paper presents a SDD and structural damage localization (SDL) method based on the partial autocorrelation function (PACF) of vibration responses. The approach is applied to a numerical model of a large WTB, where the acceleration responses are obtained from transient dynamic simulations with a simplified aerodynamic loading approach. The novel damage sensitive feature (DSF) is developed as the Mahalanobis distance between a baseline and current vector of PACF coefficients. First, numerical modal analysis of the finite element (FE) WTB model is performed in order to estimate the effect of a disbonding damage scenario on the vibration characteristics. Second, the behaviour of the PACF for time series of the healthy system is discussed. Third, the SDD results on the basis of statistical hypothesis testing are assessed for two selected sensor locations and increasing damage extents. Finally, the performance of the proposed DSF with respect to SDL is illustrated for multiple locations on the WTB’s surface. This study demonstrated the efficiency of a DSF based on the PACF for SDD and SDL, which is promising for future developments of vibration-based SHM techniques in WTBs.

Application of two-stage evaluation and optimization update methods for the structural damage detection of a portal beam structure

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

Many steel truss rail bridges in Indonesia were built decades ago. To assure that bridge structure is in-service conditions, periodic monitoring is required, thus structural health of the bridge can be assessed and sudden collapse of the structure can be avoided. In health monitoring of structure, structural damage detection methods are frequently required so that damaged/weak element of the structure can be identified and technique for repairing it can be determined. In this paper, structural damage detection method of steel truss bridge is discussed. The method applied in this study used vibration characteristics of the structure for detecting structural damages occurred in the structure. The method was utilized to identify simulated damages occurred in a steel-truss railway bridge located in Porong East Java. The bridge has span of 30 meters and it was built during the era of Dutch colonization. The structure was modeled numerically utilizing CSI Bridge v15 computer software for facilitating in obtaining its dynamic characteristics. Results of this study showed that the method could identify precisely the existence of damage took place in the structure.

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

In principal, a proper analysis of the dynamic response of a structure can provide general indicators of its operational conditions. When the dynamic response changes due to a variation of the physical properties of a structure, then one may conclude that some kind of damages has occurred. This paper presents investigation of the robustness and comparison of four simple methodologies to both identify and quantify the damages in structures, based on the use of Frequency Response Functions (FRF) signals, Principal Component Analysis technique (PCA) and Transmissibility. A steel beam with constant rectangular cross-section is used to compare the proposed approaches. At first, nine damaged scenarios are created and for each of them numerical examples are discussed; a database of FRFs is measured using modal testing. Then, PCA theory is applied to the FRF matrix and global damage detection and quantification indices are defined by using the first 3 Principal Components; Hotelling's T-squared distribution is also applied and by using transmissibility two other indicators, Transmissibility Damage Indicator and Weighted Damage Indicator, are computed for the assessment of damage. The reported examples show that all proposed methods are able to detect and quantify damages at the initial stage.

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

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