Explore the vast range of titles available.

Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted from convolutional neural networks in continuous object tracking. The spatial features retain higher resolution for precise localization and semantic features capture more semantic information and less fine-grained spatial details. Therefore, we localize the target by fusing these different features, which improves the tracking accuracy. Besides, we construct the multi-scale pyramid correlation filter of the target and extract its spatial features. This filter determines the scale level effectively and tackles target scale estimation. Finally, we further present a novel model updating strategy, and exploit peak sidelobe ratio (PSR) and skewness to measure the comprehensive fluctuation of response map for efficient tracking performance. Each contribution above is validated on 50 image sequences of tracking benchmark OTB-2013. The experimental comparison shows that our algorithm performs favorably against 12 state-of-the-art trackers.

Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

Summary Model updating based on vibration response measurements is a technique for reducing inherent modeling errors in finite element (FE) models that arise from simplifications, idealized connections, and uncertainties with regard to material properties. Updated FE models, which have relatively fewer discrepancies with their real structural counterparts, provide more in‐depth predictions of the dynamic behaviors of those structures for future analysis. In this study, we develop a full‐scale FE model of a major long‐span bridge and update the model to improve an agreement between the identified modal properties of the real measured data and those from the FE model using a Bayesian model updating scheme. Sensitivity‐based cluster analysis is performed to determine robust and efficient updating parameters, which include physical parameters having similar effects on targeted natural frequencies. The hybrid Monte Carlo method, one of the Markov chain Monte Carlo sampling methods, is used to obtain the posterior probability distributions of the updating parameters. Finally, the uncertainties of the updated parameters and the variability of the FE model's modal properties are evaluated.

Summary Structural damage identification is essentially an inverse problem. Ill‐posedness is a common obstacle encountered in solving such an inverse problem, especially in the context of a sensitivity‐based model updating for damage identification. Tikhonov regularization, also termed as ℓ2‐norm regularization, is a common approach to handle the ill‐posedness problem and yields an acceptable and smooth solution. Tikhonov regularization enjoys a more popular application as its explicit solution, computational efficiency, and convenience for implementation. However, as the ℓ2‐norm term promotes smoothness, the solution is sometimes over smoothed, especially in the case that the sensor number is limited. On the other side, the solution of the inverse problem bears sparse properties because typically, only a small number of components of the structure are damaged in comparison with the whole structure. In this regard, this paper proposes an alternative way, sparse regularization, or specifically ℓ1‐norm regularization, to handle the ill‐posedness problem in response sensitivity‐based damage identification. The motivation and implementation of sparse regularization are firstly introduced, and the differences with Tikhonov regularization are highlighted. Reweighting sparse regularization is adopted to enhance the sparsity in the solution. Simulation studies on a planar frame and a simply supported overhanging beam show that the sparse regularization exhibits certain superiority over Tikhonov regularization as less false‐positive errors exist in damage identification results. The experimental result of the overhanging beam further demonstrates the effectiveness and superiorities of the sparse regularization in response sensitivity‐based damage identification. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper presents a probabilistic damage identification study on a full‐scale structure, the Dowling Hall footbridge, through a Bayesian finite element (FE) model updating. The footbridge is located at Tufts University and is equipped with a continuous monitoring system that measures its ambient acceleration response. A set of data is recorded once every hour or when triggered by large vibrations. The modal parameters of the footbridge are extracted from each set of data and are used for FE model updating. In this study, effects of physical damage are simulated by loading a small segment of the footbridge deck with concrete blocks. The footbridge deck is divided into five segments in an FE model of the test structure, and the added mass on each segment is considered as an updating parameter. Overall, 72 sets of data are collected during the loading period, and different subsets of these data are used to find the location and extent of the damage (added mass). The adaptive Metropolis–Hastings algorithm with adaption on the proposal probability density function is successfully used to generate Markov Chains for sampling the posterior probability distributions of the five updating parameters. Effects of the number of data sets used in the identification process are investigated on the posterior probability distributions of the updating parameters. The probabilistic model updating framework accurately predicts the simulated damage and the level of confidence on the obtained results. The maximum a‐posteriori estimates of damage in the probabilistic approach are found to be in good agreement with their corresponding deterministic counterparts. Copyright © 2014 John Wiley & Sons, Ltd.

This study presents an efficient approach for the detection of damages in laminated composite beams with arbitrary lay-up. The approach uses the finite element model updating based on limited vibration data and a metaheuristic optimization algorithm. To this aim, a thirteen degrees-of-freedom (DOFs) beam finite element (FE) model is employed for numerical simulation of the actual structure. The Guyan condensation method is employed for model-order reduction to simulate the limited number of sensor data. The damage detection problem is defined as an unconstrained optimization problem. The objective function to be minimized is formulated using the objective function constructed as a weighted linear combination of the root-mean-square error in the frequencies and the error in the correlation between two mode shapes, which is represented by Modal Assurance Criterion (MAC). Teaching–Learning-Based Optimization (TLBO) is used as a metaheuristic tool for optimization. The proposed method is verified by four examples. A parametric study on anisotropic-laminated composite beams with cantilevered and clamped end conditions under three assumed damage scenarios is conducted to show the efficacy of the proposed method. The results indicate that the proposed method can identify single and multiple damages in anisotropic-laminated composite beams with adequate precision and outperforms the other algorithms in terms of accuracy and computational cost.

Digital twins efficiency lies in fast and representative solutions of inverse problems to accomodate models with physical observations. The quality of the solution of an inverse problem is conditioned by inherent features of the latter, in particular (i) the richness of available data, (ii) the a priori experimental and modeling knowledge that allows to regularize the ill-posedness nature of the problem, and (iii) the complexity of the space in which updated parameters are sought. We present in this contribution a fully automated physics-guided model updating framework dedicated to the correction of finite element models from low-frequency dynamics measurements. The proposed methodology is based on the minimization of a modified Constitutive Relation Error (mCRE) functional, whose basic idea is to construct mechanical fields and identify material parameters that are a trade-off between all available information (and associated confidence) but without any further assumption. The dependency into some expert-user’s judgment is thus avoided. Dedicated rules are provided to automatically calibrate all mCRE internal tuning parameters as well as a strategy to optimize the space in which parameters are sought, leading to a fully autonomous algorithm. The performance and robustness of the proposed model updating methodology are illustrated using synthetic ground motion tests on a bending plate in which defects of various shapes are identified from noisy acceleration datasets, with inherent limitations due to richness of input loading, sensors sparsity and defect identifiability.

Background Finite element model updating (FEMU) is an inverse technique that is used to identify material (constitutive) model parameters based on experimental data. These experimental data, often in the form of full-field strains, may be subject to a filtering bias unique to the measurement technique, which can propagate to material parameter identification error. Objective Numerically adjusting for this filtering mismatch between the finite element analysis (FEA) and experimental measurements, here from Digital Image Correlation (DIC), is necessary to produce an accurate calibration. We investigate “direct-leveling” the FEA to the DIC data, i.e. computing strains using consistent methods and length scales for both data sets, before performing model calibration. Thus, both data sets have the same spatial resolution and can be quantitatively compared more readily. Methods We generated two sets of synthetic “experimental” DIC displacement data: one directly from FEA nodal displacements and one from DIC images synthetically deformed according to the FEA displacements. We then explored how the FEMU material model parameter identification is affected by DIC user-defined settings, including virtual strain gauge size, step size, and subset shape function, as well as misalignment between the FEA and DIC datasets. Results We found that direct-levelling of the FEA data before FEMU calibration returned more accurate results. This accuracy was independent of the DIC settings and spatial resolution. In contrast, performing FEMU with the unlevelled FEA data resulted in significant biases in the identified parameters. Conclusion In FEMU-based calibrations, it is advantageous to properly level the strain from the FEA to match the filtering and spatial resolution of the DIC results.

Bayesian model updating technique has been widely investigated and utilized in the field of finite element model (FEM) updating for its advantages in system uncertainty quantification. Most existing studies focus on numerical and experimental models. More studies on large-scale civil infrastructures based on field monitoring are still required. A case study on Bayesian FEM updating of the Runyang Suspension Bridge (RSB), a long-span suspension bridge with a main span of 1,490 m, is carried out in this paper. The Bayesian updating method is utilized to update the initial FEM of RSB, aiming to make the numerical modal properties match the field monitoring results. Two stochastic sampling algorithms, i.e., the Metropolis-Hastings (MH) algorithm and the Hybrid Monte Carlo (HMC) algorithm, are respectively investigated to show their advantages and limitations in Bayesian updating. Subsequently, based on the experimentalsamples generated by the Latin hypercube sampling algorithm, a Kriging predictor is established as a surrogate model to reduce the computational burden of model updating. Results show that the HMC algorithm could guarantee much higher acceptance rate of the sampled chain than the MH algorithm especially when the updating step size is large. In addition, combined with the Kriging predictor, Bayesian model updating method could serve as an effective and efficient tool to calibrate the FEM of large-scale civil infrastructures.

Finite element (FE) models are widely used in structural health monitoring to represent real structures and assess their condition, but discrepancies often arise between numerical and actual structural behavior due to simplifying assumptions, uncertain parameters, and environmental influences. Temperature variation, in particular, significantly affects structural stiffness and modal properties, yet it is often treated as noise in traditional model updating methods. This study treats temperature changes as valuable information for model updating and structural damage quantification. The Bayesian model updating approach (BMUA) is a probabilistic approach that updates uncertain model parameters by combining prior knowledge with measured data to estimate their posterior probability distributions. However, traditional BMUA methods assume mass is known and only update stiffness. A novel BMUA framework is proposed that incorporates thermal buckling and temperature-dependent stiffness estimation and introduces an algorithm to eliminate the coupling effect between mass and stiffness by using temperature-induced stiffness changes. This enables the simultaneous updating of both parameters. The framework is validated through numerical simulations on a three-story aluminum shear frame under uniform and non-uniform temperature distributions. Under healthy and uniform temperature conditions, stiffness parameters were estimated with high accuracy, with errors below 0.5% and within uncertainty bounds, while mass parameters exhibited errors up to 13.8% that exceeded their extremely low standard deviations, indicating potential model bias. Under non-uniform temperature distributions, accuracy declined, particularly for localized damage cases, with significant deviations in both parameters.