Optimal Sensor Placement Algorithm for Structural Damage Identification

As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system.

This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine‐tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency‐domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient ( R value), and reduced mean absolute error (MAE) compared to time‐domain signals. The effectiveness of this method was verified on a six‐story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real‐world data, thereby facilitating effective structural health monitoring and damage identification.

Purpose Aiming at the problems of high cost, high risk and traffic impact of in situ load testing of Bridges after fire, this paper proposes a fire bridge structural damage identification method based on a fire dynamics simulator (FDS)-Kriging model modification and a unit modal strain energy damage index, so as to effectively assess the damage of bridge structures after fire. Determine whether it can continue to be used and whether it affects driving safety. Design/methodology/approach Propose a fire bridge structural damage identification method to establish the FDS-Kriging model correction. Findings The finite element model of the fire bridge modified using FDS-Kriging approach can reflect the actual situation of the bridge structure more accurately, and the damage location and degree identified by the modal strain energy method are consistent with the actual bridge. Therefore, the effectiveness of the proposed method has been verified, and a new strategy for fire bridge damage identification has been provided. Research demonstrates that the finite element model of the fire-damaged bridge, modified using the FDS-Kriging method, can more accurately reflect the actual structural state. Moreover, the damage location and severity identified via the modal strain energy method align well with the actual bridge conditions. Thus, the effectiveness and engineering applicability of the proposed method are validated, offering a comprehensive new strategy and technical support for the integrated performance evaluation, damage identification and reliability analysis of fire-damaged bridges. Originality/value By determining the finite element correction interval and combining it with the Kriging model, the method realizes the fine correction of the finite element model of the fire bridge. Structural damage identification is carried out by using the unit modal strain energy damage index. This verifies the effectiveness and engineering applicability of the proposed method, which offers a novel approach and technical support for the comprehensive performance evaluation, damage identification and reliability analysis of fire-damaged bridges.

Structural nonlinear damage identification based on Bayesian optimization GNAR/GARCH model and its experimental study

This paper presents a structural damage identification approach based on the time domain impulse response functions, which are extracted from the measured dynamic responses with the input available. The theoretical sensitivity of the impulse response function with respect to the system stiffness parameters considering the damping model is derived. The first-order sensitivity based model updating technique is performed for the iterative model updating. The initial structural finite element model and acceleration measurements from the damaged structure are required. Local damage is identified as a reduction in the elemental stiffness factors. The impulse response function sensitivity based optimal sensor placement strategy is employed to investigate the best sensor locations for identification. Numerical studies on a beam model are conducted to validate the proposed approach for the extraction of time domain impulse response functions and subsequent damage identification. The simulated damage can be identified effectively and accurately.

<p>An experimental implementation of a relative wavelet entropy (RWE)-based structural damage identification technique (DIT) is presented. The technique is capable of detecting and localizing structural damage, as well as estimating its severity, without the need for any data to be collected from undamaged (reference) state of structure. The bases of this reference-free DIT are: (1) structural damage changes the energy distribution of bridges’ vibrational signals; (2) these changes are detectable by means of discrete wavelet transforms (DWTs); and (3) the detected changes can be quantified using spectral entropy. The efficacy of the proposed RWE-based DIT in identification of structural damage has been verified through its application on a precast bridge truss girder system tested under fatigue loading. The girder consists of glass fibre-reinforced polymer (GFRP) tubes filled with concrete reinforced and connected to pretensioned top and bottom concrete chords by double-headed GFRP bars.‌</p>

The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan-based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

Vibration-based damage identification and structural health monitoring have been developed in recent years. However, it is still a challenge to effectively extract the information of structural damage from a large amount of monitoring data and to realize the rapid detecting damages and assessing structural conditions of bridge structures under seismic excitation. To address this problem, transmissibility function (TF) is used in this paper, and based on the seismic monitoring data of bridge structures, an intelligent algorithm integrating transmissibility function and support vector machine (SVM) is developed for multi-level/scale structural damage identification under seismic excitation. Firstly, the TF is calculated with the measured structural vibration responses. Then, the indicator takes the sum of transmissibility along the specific frequency range is used as input for SVM. Finally, the classification algorithm of support vector machine is employed to structural damage alarming under earthquake. The proposed method could eliminate the influence of external excitations because transmissibility function only depends on the measured seismic responses of structures and the information of external excitations is not required. Further, the proposed method is sensitive to the structural damage. To illustrate the performances and feasibility of the proposed algorithm, the damage identification of numerical simulation under seismic excitation is studied.

Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN)

Quantifying uncertainty for structural damage identification in the presence of model errors from a deterministic sensitivity-based regime

The present work addresses the problem of structural damage identification built on the statistical inversion approach. Here, the damage state of the structure is continuously described by a cohesion parameter, which is spatially discretized by the finite element method. The inverse problem of damage identification is then posed as the determination of the posterior probability densities of the nodal cohesion parameters. The Markov Chain Monte Carlo method, implemented with the Metropolis–Hastings algorithm, is considered in order to approximate the posterior probabilities by drawing samples from the desired joint posterior probability density function. With this approach, prior information on the sought parameters can be used and the uncertainty concerning the known values of the material properties can be quantified in the estimation of the cohesion parameters. The assessment of the proposed approach has been performed by means of numerical simulations on a simply supported Euler–Bernoulli beam. The damage identification and assessment are performed considering time domain response data. Different damage scenarios and noise levels were addressed, demonstrating the feasibility of the proposed approach.

In the past few decades, various techniques for structural damage identification based on dynamic properties have been widely studied, among which modal curvature is the research hotspot, due to its high sensitivity to local damage. To realize story-level structural damage identification of shear buildings, this paper first derives the patterns of variation in modal curvature of the damaged structure. Then, a new damage index sensitive to local damage called the Modal Curvature Change modified with Softmax (MCCSM for short), is proposed to improve the accuracy of structural damage identification. Afterward, the validity of the damage index is verified by a shaking table test of a scaled RC frame structure model and its corresponding numerical model. The research results show that the damage location indicated by MCCSM corresponds well with the damage phenomenon in the shaking table test and numerical simulation; compared with using the theoretical mode shape, the MCCSM index can give comparable damage identification results using the mode shape obtained through operational modal analysis from vibration time-history data, which proves the feasibility in engineering practices; through numerical simulation, it is proved that the MCCSM index is applicable in a wide range of damage cases with different story position, number of damaged story, and damage extent.

One outstanding obstacle that hinders robust application of vibration-based damage identification to civil structures is that the number of sensors installed on a large civil structure is always limited, compared with the total degrees of freedom of the structure, so that the limited measured responses may not provide enough information for detecting local damage. Furthermore, developments in sensor technology make installation of heterogeneous sensors on a structure practical and feasible while every type of sensor has its own merits and drawbacks for damage identification. But the benefits of utilizing heterogeneous sensors in vibration-based damage identification have not been fully investigated. This study proposes a damage identification method by combining the response reconstruction technique with the response sensitivity–based finite element model updating method to address these issues. The number and location of heterogeneous sensors, such as accelerometers, displacement transducers, and strain gauges, are optimally and collectively determined in an optimization strategy to obtain the best reconstruction of multi-type responses of a structure using Kalman filter. After damage occurrence, radial basis function network is employed to predict the mode shapes using the modal properties extracted from the measurement data by experimental modal analysis method, and these modal properties are further used to reconstruct responses of the damaged structure. The reconstructed responses are finally used to identify the damage in terms of sensitivity-based finite element model updating. In every updating, the sparse regularization is employed to increase the identification accuracy. A simply supported overhanging steel beam composed of 40 elements serves as a numerical study to demonstrate the procedure and feasibility of the proposed method. The validation of this method is further conducted by laboratory test. Both simulation study and laboratory test show that the multi-sensing approach via response reconstruction does improve the identification accuracy of damage location and quantization considerably.

Structural damage identification based on metaheuristic algorithms is an important part of structural health monitoring with great potential. However, the metaheuristic intelligent algorithms probably have flaws of slow convergence speed and low calculation accuracy, which need to be improved to address engineering optimization problems. In this paper, the black widow optimization (BWO) algorithm is used for structural damage identification. In addition, a multistrategy fusion–improved BWO (IBWO) algorithm is proposed by introducing the tent chaotic mapping, the golden sine equation, the gazelle wandering equation, and the boundary treatments. First, in the population initialization stage, tent chaotic mapping is introduced to improve the quality of the initial solution. Second, the golden sine strategy is used to acquire the optimal solution quickly in local search. Then, the motion equation of the gazelle algorithm is employed to enhance the global search ability and avoid the algorithm falling into the local optimal solution. Finally, the boundary processing strategy is presented to reduce the calculation of solutions and improve the optimization efficiency. A novel damage identification objective function is redefined by combining the modal assurance criterion and the modal flexibility. Then, a two‐story rigid frame structure is utilized for numerical simulations. Moreover, experimental studies with a simply supported beam were carried out to verify the performance of the proposed damage identification method. Simulation results and experimental studies demonstrate that, even with the interference of strong noise, the IBWO algorithm has a higher accuracy and efficiency in damage identification compared to the BWO algorithm.

No AccessTechnical NoteClustering of Sensor Locations Using the Effective Independence MethodMichael I. Friswell and Rafael Castro-TrigueroMichael I. FriswellCollege of Engineering, Swansea University, Swansea SA2 8PP, United Kingdom*Professor of Aerospace Structures, College of Engineering; .Search for more papers by this author and Rafael Castro-TrigueroDepartment of Mechanics, University of Cordoba, 14071 Cordoba, Spain†Department of Mechanics, Campus de Rabanales.Search for more papers by this authorPublished Online:12 Mar 2015https://doi.org/10.2514/1.J053503SectionsView Full TextPDFPDF Plus ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Pickrel C. R., “A Practical Approach to Modal Pretest Design,” Mechanical Systems and Signal Processing, Vol. 13, No. 2, 1999, pp. 271–295. doi:https://doi.org/10.1006/mssp.1998.1212 0888-3270 CrossrefGoogle Scholar[2] Penny J. E. T., Friswell M. I. and Garvey S. 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