Search

Quantifying the uncertainty in the dynamic properties of large-scale complex engineering structures presents significant computational challenges. Monte Carlo simulation (MCS) method is extensively employed to perform uncertainty quantification (UQ) because of its generality, stability, and easy implementation. However, a brute-force MCS approach may be unaffordable and impractical when the target model contains a large number of uncertain parameters. In this circumstance, MCS requires a potentially burdensome (if not computationally intractable) number of model evaluations to obtain a credible estimate of the global statistics. In this study, a general framework for analytical UQ of model outputs using a Gaussian process (GP) metamodel is presented, where case inputs are characterized as normal and/or uniform random variables. A detailed derivation of important low-order statistical moments (mean and variance) is given analytically. This analytical method is adopted to characterize the uncertainty of modal frequencies of two bridges with assumed normally- and uniformly-distributed parameters. Meanwhile, the brute-force MCS approach is used for comparison of GP metamodel-derived statistics. Results show that the GP method outperforms the MCS methodology in terms of computational cost, with consistency in the "true" values obtained by MCS. It demonstrates that this GP method is feasible and reliable for modal frequency UQ of complex structures. © 2014 Elsevier Ltd.

A high-strength bolt connection is the key component of large-scale steel structures. Bolt loosening and preload loss during operation can reduce the load-carrying capacity, safety, and durability of the structures. In order to detect loosening damage in multi-bolt connections of large-scale civil engineering structures, we proposed a multi-bolt loosening identification method based on time-frequency diagrams and a convolutional neural network (CNN) using vi-bro-acoustic modulation (VAM) signals. Continuous wavelet transform was employed to obtain the time-frequency diagrams of VAM signals as the features. Afterward, the CNN model was trained to identify the multi-bolt loosening conditions from the raw time-frequency diagrams intelligently. It helps to get rid of the dependence on traditional manual selection of simplex and ineffective damage index and to eliminate the influence of operational noise of structures on the identification accuracy. A laboratory test was carried out on bolted connection specimens with four high-strength bolts of different degrees of loosening. The effects of different excitations, CNN models, and dataset sizes were investigated. We found that the ResNet-50 CNN model taking time-frequency diagrams of the hammer excited VAM signals, as the input had better performance in identifying the loosened bolts with various degrees of loosening at different positions. The results indicate that the proposed multi-bolt loosening identification method based on VAM and ResNet-50 CNN can identify bolt loosening with a reasonable accuracy, computational efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Nonlinear dynamic behaviors of civil engineering structures have been observed not only under extreme loads but also during normal operations. Characterization of the time-varying property or nonlinearity of the structures must account for temporal evolution of the frequency and amplitude contents of nonstationary vibration responses. Neither time analysis nor frequency analysis method alone can fully describe the nonstationary characteristics. In this article, an attempt is made to review the milestone developments of time–frequency analysis in the past few decades and summarize the fundamental principles and structural engineering applications of wavelet analysis and Hilbert transform analysis in system identification, damage detection, and nonlinear modeling. This article is concluded with a brief discussion on challenges and future research directions with the application of time–frequency analysis in structural engineering. [ABSTRACT FROM AUTHOR]

The structural health monitoring (SHM) involves the sampled operational vibration measurements over time so that the structural features can be extracted accordingly. The recurrence plot (RP) and corresponding recurrence quantification analysis (RQA) have become a useful tool in various fields due to its efficiency. The threshold selection is one of key issues to make sure that the constructed recurrence plot contains enough recurrence points. Different signals have in nature different threshold values. This paper is aiming at presenting an approach to determine the optimal threshold for the operational vibration measurements of civil engineering structures. The surrogate technique and Taguchi loss function are proposed to generate reliable data and to achieve the optimal discrimination power point where the threshold is optimum. The impact of selecting recurrence thresholds on different signals is discussed. It is demonstrated that the proposed method to identify the optimal threshold is applicable to the operational vibration measurements. The proposed method provides a way to find the optimal threshold for the best RP construction of structural vibration measurements under operational conditions. [ABSTRACT FROM AUTHOR]

Operational modal analysis subjected to ambient or natural excitation under operational conditions has recently drawn great attention. In this article, the power spectrum density transmissibility (PSDT) is proposed to extract the operational modal parameters of a structure. It is proven that the PSDT is independent of the applied excitations and transferring outputs at the system poles. As a result, the modal frequencies and mode shapes can be extracted by combing the PSDTs with different transferring outputs instead of different load conditions where the outputs from only one load condition are needed. A five-story shear building subjected to a set of uncorrelated forces at different floors is adopted to verify the property of PSDTs and illustrate the accuracy of the proposed method. Furthermore, a concrete-filled steel tubular half-through arch bridge tested in the field under operational conditions is used as a real case study. The identification results obtained from currently developed method have been compared with those extracted from peak-picking method, stochastic subspace identification, and finite element analysis. It is demonstrated that the operational modal parameters identified by the current technique agree well with other independent methods. The real application to the field operational vibration measurements of a full-sized bridge has shown that the proposed PSDTs are capable of identifying the operational modal parameters (natural frequencies and mode shapes) of a structure. [ABSTRACT FROM AUTHOR]

A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients. [ABSTRACT FROM AUTHOR]

• A method is proposed for damage identification under nonstationary excitations. • UARDM is a new type of RP that represents dynamic characteristics of the structure. • Multi-label CNN model decouples the identification of damage locations and levels. • The proposed method performs with higher identification accuracy and efficiency. Civil engineering structures inevitably suffer from nonstationary ambient excitations in practice, which make conventional damage identification methods relying on the stationary assumption ineffective. This study presents a novel method based on unthresholded assembled recurrence distance matrix (UARDM) and multi-label convolutional neural network (CNN) for structural damage identification under nonstationary excitations. UARDM is a new type of recurrence plot (RP) that is proposed to integrate information of multiple channels and dispense with the artificially selected threshold. It reveals intrinsic dynamic characteristics of the structure using its vibration responses from the perspective of global probabilistic autocorrelation. After that, CNN is applied to automatically extract damage-sensitive features of UARDMs and classify them for the identification of damage cases. Instead of the traditional single-label CNN model that labels each combination of damage location and level as an objective class, the multi-label CNN model is developed to decouple the identification processes of damage locations and levels in order to improve the identification accuracy and computational efficiency. It evaluates the damage level at each location through a sub-branch with an independent set of labels and detects the damage locations by fusing information of all the sub-branches. A comprehensive comparison was conducted among single-label and multi-label CNN models input with raw accelerations, unthresholded multivariate recurrence plots (UMRPs), unthresholded recurrence plots (URPs) and UARDMs through numerical simulation and experimental test. It was demonstrated that the proposed structural damage identification method based on UARDM and multi-label CNN was able to identify multiple damage locations and levels under various stationary and nonstationary excitations with higher accuracy, efficiency and robustness, and even able to detect multiple-damage cases that were not measured beforehand and involved in the training dataset. [ABSTRACT FROM AUTHOR]

• An improved LMSST with adaptive window width (ALMSST) method is proposed to identify the instantaneous frequencies (IFs) of time-varying structures. • The window width of ALMSST can be adaptively determined by combining AR-VMD and a window width optimization algorithm. • Numerical simulations and experiments verified the applicability of the proposed ALMSST method for IF identification of time-varying structures. Civil engineering structures usually exhibit time-varying characteristics under operational conditions due to changes in environment and loads. The identification of instantaneous frequencies (IFs) from measured responses of time-varying structures is a challenge when using the local maximum synchrosqueezing transform (LMSST) due to the difficulty in selecting the appropriate window width. In this study, an improved LMSST with adaptive window width (ALMSST) is proposed to circumvent the limitations of LMSST. The window width of ALMSST can be adaptively determined by combining the autoregressive power spectrum-based variational modal decomposition (AR-VMD) and a window width optimization algorithm. The AR-VMD is used to decompose the multi-component signal into mono-component signals. The Rényi entropy is adopted as an evaluation index in the window width optimization algorithm for selecting the optimal window width for each mono-component signal. Therefore, ALMSST can provide a highly concentrated time–frequency (TF) representation for all mono-component signals. Two simulated signals demonstrate that the ALMSST improves the accuracy of identified IFs compared with LMSST. Numerical simulation of a three-story shear building model with time-varying stiffness shows that ALMSST can accurately identify the IFs of time-varying structures under heavy noise. A cable test with linear and sinusoidal varying tension forces and a vehicle-bridge interaction (VBI) model system with different vehicle weights are investigated to verify the applicability of ALMSST to track the IFs of time-varying structures. Numerical simulation and experimental results illustrate that ALMSST performs well in identifying the IFs of time-varying structures. [ABSTRACT FROM AUTHOR]

• A combined method is proposed for instantaneous frequency identification. • The method combines the extended AMD, recursive Hilbert transform and zoom SWT. • It enables to provide good time-frequency representation for non-asymptotic signals. Civil engineering structures such as high-rise buildings and long-span cable-supported bridges usually exhibit low-frequency characteristics and the resultant response signals may be non-asymptotic (the amplitude change rate is higher than its phase change rate) and even have closely-spaced frequency components. The identification of satisfactory instantaneous frequencies of such response signals is a challenge faced by standard synchrosqueezing wavelet transform. The paper aims to propose a combined method to address this challenge. The proposed method combines an extended analytical mode decomposition (AMD) method, a recursive Hilbert transform and a zoom synchrosqueezing wavelet transform (consisting of frequency-shift operation and partial zoom synchrosqueezing operation). In the method, a multi-component signal, which may consist of closely spaced frequency components, is firstly decomposed into several mono-component signals by the extended AMD, and the extracted mono-components are then demodulated into asymptotic signals by recursively using the Hilbert transform. After that, a frequency-shift operation is employed to improve time resolution and a partial zoom synchrosqueezing operation is then applied to improve the frequency resolution in the narrow low frequency range of interest. Two numerical examples, an experiment on an aluminum cantilever beam with abrupt mass reduction and an experiment on a cable with time-varying tension forces are provided to illustrate the effectiveness, accuracy and robustness of the method. [ABSTRACT FROM AUTHOR]