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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]

This study focuses on the acoustic emission wave classification for the sake of more accurate and comprehensive rail crack monitoring in the field typically with complex cracking conditions, high-operational noise, and mass data. There are mainly three types of acoustic emission waves induced by operational noise, impact, and crack propagation, respectively. Synchrosqueezed wavelet transform was introduced to represent intrinsic characteristics of acoustic emission waves more clearly in the time–frequency domain, where acoustic emission waves induced by different mechanisms were found to show various patterns of energy distribution. Then, a multi-branch convolutional neural network model with two branches was developed to automatically classify the three types of acoustic emission waves by taking into account their synchrosqueezed wavelet transform plots in various time–frequency scales. Training, validation, and test data sets were constructed using acoustic emission waves collected through a series of field and laboratory tests with various noise levels and loading conditions. The transfer learning was used to train the model faster, and the Bayesian optimization algorithm was applied to tune the hyperparameters. Finally, the multi-branch convolutional neural network model achieved higher accuracy and robustness than the traditional convolutional neural network model of single branch in identifying different acoustic emission mechanisms. The proposed acoustic emission wave classification method based on synchrosqueezed wavelet transform and multi-branch convolutional neural network is able to detect not only surface rail cracks, where both impact-induced and crack propagation-induced acoustic emission waves would be identified, but also internal rail cracks where only crack propagation-induced acoustic emission waves would be captured. [ABSTRACT FROM AUTHOR]

This paper presents a new integrity assessment and damage localization method for piles based on one-dimensional wave propagation theory by integrating the analytical mode decomposition (AMD), recursive Hilbert transform (RHT) and complex continuous wavelet transform (CCWT) into a single assessment tool. The AMD is first used as a band pass filter to extract the mono-component over a frequency band of interest from the response of a pile head, aimed at attenuating the interference from various noisy signals. Then, the mono-component signal is demodulated into a purely frequency-modulated signal by means of RHT, which greatly reduces the interferences from the amplitude-modulated function. Finally, the CCWT is utilized to process the frequency-modulated signal and to calculate phase angles; the latter are subsequently mapped into the time–frequency domain to localize pile damage. The methodology is verified by a numerical example, in which a concrete pile is modeled by the finite element method considering the soil-pile interaction, and by an experimental case study on an actual pile. The results from the numerical and experimental examples demonstrate that the proposed method improves the efficiency of damage identification when compared with other three methods (AMD + CCWT , RHT + CCWT and CCWT). In addition, the proposed method enables the localization of damage in full-scale piles situated in soil with an acceptable engineering accuracy by mutual validation with other pile integrity assessment methods, e.g. the ultrasonic emission method. [ABSTRACT FROM AUTHOR]

Time-varying parameter identification is an important research topic for structural health monitoring, performance evaluation, damage diagnosis, and maintenance. Practical civil engineering structures usually contain multiple degrees of freedom; however, damage often locally occurs. In this study, a discrete wavelet transform and substructure algorithm is presented for tracking the abrupt stiffness degradation of shear structures. A substructure model is built by the extraction of the local structure which may contain damaged region. Time-varying stiffness and damping are expanded into multi-scales using discrete wavelet analysis. An optimization method based on Akaike information criterion is introduced to select the decomposition scale. The expanded scale coefficients are evaluated using least square method, then the original time-varying stiffness or damping parameter is identified by reconstructing from the scale coefficients. To validate the proposed method, a numerical example of seven-story shear structure with time-varying stiffness and damping is proposed. Experiment for a three-story shear-type structure with abrupt stiffness degradation is also tested in the laboratory. Both numerical and experimental results indicate that the proposed method can effectively identify the abrupt degradation of stiffness parameter with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Abstract: A synchrosqueezed wavelet transform combined by wavelet analysis and time–frequency reallocation method was recently developed to improve the quality of time–frequency representation and construct the components of a signal. However, the quality of this transform highly depends on the parameters of wavelet functions. To overcome the effects of wavelet function parameters on signal decomposition, an analytical mode decomposition method was proposed to reconstruct each component. The analytical mode decomposition selects the bisecting frequency between any two adjacent instantaneous frequencies estimated from a preliminary synchrosqueezed wavelet analysis and analytically extracts the individual components from a signal between any two bisecting frequencies. Two examples including a frequency modulated amplitude decayed signal, and seismic responses of a two-story building are analyzed. It is demonstrated that the analytically derived components are insensitive to the selection parameters of wavelet function and bisecting frequency. The proposed method is superior to the synchrosqueezed wavelet transform method for dynamic signal reconstruction. [Copyright &y& Elsevier]

In this work, continuous wavelet transformation are introduced to analyze the dynamic strain response of bridges caused by passing vehicles. The wavelet coefficients of specific scales are extracted, which clearly correlates the peak points with the vehicle axle information and made up the deficiency of traditional peak value method in identifying short wheelbase. Numerical examples of vehicle-bridge coupling vibration model indicated that the presented wavelet-based vehicle axles identification method can achieve sufficient accuracy even under the conditions of high velocity and rough road surface. Useful information can be picked up from the primary signal, and simultaneously the influence of interferential signal can be reduced efficiently by choosing specific scale corresponding to the excitation frequency. [ABSTRACT FROM AUTHOR]

Abstract: A method based on the wavelet ridges of continuous wavelet transform is proposed for the instantaneous frequency identification of time-varying structures. To eliminate noise effect, a penalty function is imposed first, and then the dynamic optimization technique is implemented for wavelet ridge extraction. The instantaneous frequencies are then identified from the extracted wavelet ridges. To validate the proposed method, a numerical example of a two degree-of-freedom spring–mass–dashpot system with time-varying stiffness is studied. The numerical results indicated that the proposed method is efficient in extracting wavelet ridges and tracking the instantaneous frequencies of the time-varying system even the numerical responses are contaminated by 10% Gauss white noise. An experiment on the cable with varying tension force is set up in the laboratory. The time-varying natural frequency is achieved by applying linear and sinusoidal varying tension forces respectively. The instantaneous cable frequencies are then identified by the proposed method. The experiment results further verified that the proposed method can effectively identify the instantaneous frequencies of time-varying structures with a high accuracy. [Copyright &y& Elsevier]

The grouting quality of tendon ducts is very important for post-tensioning technology in order to protect the prestressing reinforcement from environmental corrosion and to make a smooth stress distribution. Unfortunately, various grouting defects occur in practice, and there is no efficient method to evaluate grouting compactness yet. In this study, a method based on wavelet packet transform (WPT) and Bayes classifier was proposed to evaluate grouting conditions using stress waves generated and received by piezoelectric transducers. Six typical grouting conditions with both partial grouting and cavity defects of different dimensions were experimentally investigated. The WPT was applied to explore the energy of received stress waves at multi-scales. After that, the Bayes classifier was employed to identify the grouting conditions, by taking the traditionally used total energy and the proposed energy vector of WPT components as input, respectively. The experimental results demonstrated that the Bayes classifier input with the energy vector could identify different grouting conditions more accurately. The proposed method has the potential to be applied at key spots of post-tensioning tendon ducts in practice. [ABSTRACT FROM AUTHOR]

• An S-transform-based time-varying characteristic analysis method is proposed to extract the frequency fluctuations. • The approximation description between structural instantaneous frequency and S-transform is deducted. • A novel Gaussian window function is proposed for S-transform to improve the time-frequency resolution. • The laboratory vehicle-bridge dynamic interaction experiment is conducted for verification. • Parametric study involving road surface roughness are discussed in the numerical simulation. The health monitoring of bridge under moving vehicles demands a high-efficient time-varying characteristic analysis method to extract the frequency fluctuations caused by vehicle-bridge dynamic interaction effect. Characteristics identification of structures becomes a challenging task especially when time-varying vibration modes are involved. For this purpose, an instantaneous frequency identification technique based on modified S-transform reassignment is presented in this paper. Firstly, the general approximation between structural instantaneous frequency and S-transform is deduced. The Gaussian window is employed in S-transform and a novel first-order frequency function with two parameters is added into the Gaussian window function to improve the time–frequency resolution. The added function makes the Gaussian window scalable-sliding and the optimal values of the parameters are selected based on time–frequency concentration criterion. Considering the Rényi entropy as the measurement of criterion, the information performance extracted from S-transform spectrogram is evaluated. Then the time–frequency reassignment technique is applied to improve the readability of S-transform spectrogram to assure the accuracy of the extracted instantaneous characteristics, which enhances the time–frequency concentration efficiently. The proposed method is verified by numerical simulation, the results obtained by the spectrograms derived from short-time Fourier transform, wavelet transform, modified S-transform and reassigned modified S-transform are compared. Afterwards, the time-varying frequency components related to the vehicle-bridge interaction are extracted from the response signal. Numerical simulations involving different parameter analysis and laboratory experiments are conducted to validate the flexibility of the technique in vehicle-bridge system. The effect of vehicle weight, speed and road surface roughness on the identified results are discussed. Both numerical and experimental results have demonstrated that a higher time–frequency concentration in the spectrogram can be observed of the proposed modified S-transform reassignment method than other time–frequency methods, and subsequently a higher accuracy of instantaneous frequency identification is achieved which provides a good tool for time-varying characteristic analysis in bridge health monitoring. [ABSTRACT FROM AUTHOR]

Vehicle and bridge systems (VBSs) are subjected to stochastic excitations. Significant research effort has been devoted to the stochastic response analysis of VBSs. In most previous studies, VBSs have been treated as deterministic systems with certain parameters. However, the real VBSs always have uncertain parameters, such as the mass and spring stiffness of the vehicles and elastic modulus of the bridge material, which significantly affect the response power spectrum (RPS) estimation. Moreover, the equations of motion of the VBSs are essentially second-order differential equations with time-dependent and random coefficient matrices, and the excitation-response spectral relation cannot be directly described using the frequency response functions. These properties render the available methods unsuitable for the RPS estimation of the VBSs when both structural uncertainty and stochastic excitations are considered. In the present study, a harmonic-wavelet approach is proposed to estimate the time-varying RPSs of the VBSs with random parameters. The second-order differential equations of motion of VBSs with random parameters are first transformed into linear algebraic equations with random coefficient matrices using a harmonic-wavelet analysis technique; then, the excitation-response spectral relation of VBSs is derived based on these linear algebraic equations. Finally, the RPSs of VBSs with uncertain parameters are solved using the perturbation technique. The accuracy of the proposed method was verified by comparing it with the Monte Carlo method using a numerical example of railway VBS. The numerical simulation results show that the parameter uncertainties significantly affect the RPSs of the VBSs and increase the amplitude of the time-varying RPSs. The uncertainty of vehicle stiffness was the most sensitive factor for vehicle RPSs, and the bridge elastic modulus was the most sensitive factor for the bridge RPSs. [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]