Structural Damage Identification with Extracted Impulse Response Functions and Optimal Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural 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.

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.

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.

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.

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.

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 using the impedance/admittance measurements of a piezoelectric transducer can be converted into a multi-objective optimization framework targeting the minimization of the discrepancy between prediction and experimental measurements, with damage locations and severities as unknown variables. However, the unknowns are usually on a large scale and show sparse characteristics since the damage only occurs at a small area. This places the burden on the optimization algorithms in the identification process. Here, a sparse initialization algorithm is introduced to generate a sparse population for the large-scale variables to tackle the challenge. The algorithm is combined with the particle swarm algorithm to locate and quantify the damage for verification purposes. Several cases are considered, and the results show that the algorithm can generate high-quality damage identification solutions with limited simulation and experimental measurements.

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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Security concerns about water distribution networks (WDNs) have led to increased interest in optimizing sensor locations in WDNs achieved through a calibrated hydraulic model. This paper presents a methodology, which consists of two stages. The first stage consists of calibration of a hydraulic model using a genetic algorithm (GA). A real-life network of one of the hydraulic zones of Nagpur city, India, is considered, which optimizes the settings of a throttled controlled valve at different timings for calibration. In this stage, a detailed case study, GA calibration model, methodology and results of calibrated models are discussed. The second stage consists of identifying optimal sensor locations using a newly developed software tool named ‘S-PLACE GA’ and its efficiency and effectiveness are discussed. It can be used for the dual purpose of routine monitoring of water quality and for early detection of contamination. The optimal locations are obtained considering two objective metrics, ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. These two objectives are combined into a single objective by using weights. Key features, input data required for the software and their applications on (1) BWSN network 1 and result comparison with others and (2) calibrated model of the first stage are discussed. Results showed the effectiveness of S-PLACE GA for practical applications.

In modal testing, the quality of vibration signals and clarity of peak points to extract the natural frequencies, corresponding mode shapes and damping ratio depend on the number and location of the sensors. High measurement costs are required for structural identification and long-term structural health monitoring in large structures, which require a high number of sensors. Therefore, the minimum number of sensors should be placed at appropriate locations on the system during experimental measurements to ensure both information of sufficient quality and cost reductions. The aim of this study is to perform cost-efficient non-destructive modal tests for a laminated composite and steel cantilever beams using an optimal sensor placement approach. Finite element models of the sample beams are constituted in ANSYS® software to determine the initial candidate set of sensor locations. Then, ambient vibration tests are conducted. Based on the experimental modal amplitudes and mode shapes, optimal sensor locations are determined using the effective independence method, and measurements are repeated. The study shows that there is good agreement between the natural frequencies and mode shapes obtained from the initial measurements and those obtained using the limited number of sensors.

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

Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification.

Damage identification of a target substructure with moving load excitation

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.