Acceleration Response Signals Research Articles

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

AbstractConsidering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second‐order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first‐order modal shape curve is constructed using the second‐order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on‐site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

Research on damage identification of jacket structure under wind and wave loads based on variational mode decomposition and chirplet transform

To conduct damage localization and damage quantification of jacket structure, a new damage identification approach based on variational mode decomposition (VMD) and chirplet transform (CT) is firstly introduced. Three kinds of environmental loads, namely, regular wave load, irregular wave load, and wind load, are set on the jacket structure to simulate the real environment of the jacket as much as possible. The acceleration response signals of each node of the damaged bar are extracted and analyzed by VMD and CT. The proposed method is used to analyze the time-frequency energy changes before and after damage for damage localization. Then, the time-frequency entropy of the damage location is further calculated for damage quantification. The feasibility of the introduced method is verified by setting different operating conditions, and the results show that the proposed method can locate and quantify the damage of the jacket under different environmental loads.

Indirect identification of bridge damage based on coupled vehicle–bridge vibration and 2D-CNN

This study puts forth a methodology to discern structural damage in bridges that employs two-dimensional convolutional neural network (2D-CNN), which is rooted in the principles of continuous wavelet transform (CWT) theory. The method combines the vehicle–bridge coupled vibration response with deep learning models to extend the application of indirect bridge damage identification methods. To test the proposed method, a spatial vehicle and bridge computational model is established for a three-span continuous beam bridge, and bridge damage is simulated by reducing the stiffness of the unit under different damage conditions. Considering the stochastic nature of road roughness, a self-developed vehicle–bridge coupled vibration analysis program is utilized to acquire the vehicle acceleration response signal and construct the dataset. The 2D-CNN model, with its high sensitivity to 2D data features, is used to extract features from the vehicle vertical acceleration vibration signal. The signal undergoes transformation via CWT, resulting in a 2D grayscale time-frequency image. This image is subsequently utilised as input to construct the 2D-CNN model. Results demonstrate that this method performs well in the identification of bridge structural damage, exhibiting high accuracy in identifying the location and severity of such damage. Thus, a novel avenue is provided for the identification and assessment of bridge structural damage.

Study on the Dynamic Stability and Spectral Characteristics of a Toppling Dangerous Rock Mass under Seismic Excitation

To evaluate the dynamic stability of dangerous rock masses under seismic excitation more reasonably, a mass viscoelasticity model was adopted to simulate the two main controlling surfaces of a toppling dangerous rock mass. Based on the principles of structural dynamics, a dynamic response analysis model and motion equations were established for toppling dangerous rock masses. The Newmark-β method was utilized to establish a calculation method for the dynamic stability coefficient of a toppling dangerous rock mass. This method was applied to the WY2 dangerous rock mass developed in a steep cliff zone in Luoyi Village, and the dynamic stability coefficient time history was calculated. Subsequently, the acceleration response signals of the dangerous rock mass in different directions were analyzed using wavelet packet transform. The results show that the sum of the energy proportions of the first to third frequency bands in the n1 and s2 directions exceeded 95%. This suggests that the n1 and s2 directions of the WY2 dangerous rock mass suffered the initial damage under bidirectional seismic actions. Finally, the marginal spectra variations of the acceleration response signals in different directions were analyzed based on the HHT. The results show that the seismic energy in the n1 and s2 directions of the dangerous rock mass was found to be the most significant under seismic loading, indicating that the rock mass experienced the most severe damage along these two directions. This reveals that the failure mode of the dangerous rock mass is inclined toppling, consistent with the results of wavelet packet analysis.

Seismic damage identification of high arch dams based on an unsupervised deep learning approach

In actual engineering scenarios of arch dams, the incompleteness and nonstationarity of dynamic monitoring signals limit the accurate cognition of the health state. The effectiveness and robustness of damage characteristics in complex environments restrict the practical application of damage diagnosis theory. In this study, guided by the direct extraction of damage sensitivity features from the acceleration response signals of the arch dam, a seismic damage identification approach of high arch dams based on unsupervised learning is developed. Aiming at the problems of low measurement accuracy and poor identification robustness in the existing artificially designed damage-sensitive features, a denoising contractual sparse deep auto-encoder (DCS-DAE) model is proposed by exploring the mapping relationship between monitoring data and the structural state. This model integrates the advantages of denoising auto-encoder, compressive auto-encoder, and sparse auto-encoder. On this basis, based on the principle of reconstruction error and small probability, combined with box-plot and WKNN algorithm, a damage identification framework based on DCS-DAE is constructed. The effectiveness and noise resistance of the proposed method are verified by an extremely high arch dam. The results demonstrate that the damage identification framework based on multi-objective DCS-DAE constructed in this paper only requires the vibration information of the structure in the intact scenario, which provides a solution with higher stability and robustness for the seismic damage identification of high arch dams under strong noise pollution.

Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network

In the field of structural health monitoring (SHM), with the mature development of artificial intelligence, deep learning-based structural damage identification techniques have attracted wide attention. In this paper, the convolutional neural network (CNN) is used to extract the damage feature of simple supported steel beams. Firstly, the transient dynamic analysis of the steel beam is carried out by finite element software, and the acceleration response signals under different damage scenarios are obtained. Then, the acceleration response signal is decomposed by wavelet packet decomposition (WPD) to extract the wavelet packet band energy ratio variation (ERV) index as the training sample of CNN. Subsequently, the vibration experiment of a simple supported steel beam was carried out, and the results were compared with the numerical simulation results. The characteristic indexes were obtained by making corresponding changes to the vibration signal, and then, the experimental data were input into the CNN to predict the effect of damage detection. The results show that the method can successfully detect the intact structure, single damage, and multiple damages with an accuracy of 95.14% under impact load, and the performance is better than that of support vector machine (SVM), with good robustness.

Research on the Identification Method of Overhead Transmission Line Breeze Vibration Broken Strands Based on VMD−SSA−SVM

For the problem of overhead line broken strand identification, a VMD−SSA−SVM-based overhead transmission line broken strand identification method is proposed. First, the vibration acceleration response signal of overhead transmission line under breeze environment was simulated by Ansys simulation software. Then the variable modal decomposition of the vibration acceleration response of the transmission line is performed to obtain the intrinsic mode function corresponding to the vibration behavior of the transmission line. The Hilbert transform is used to analyze the intrinsic mode functions of each order, and the Hilbert spectrum and marginal spectrum of the intrinsic mode functions of each order are studied to extract the first six order intrinsic frequencies. Each set of intrinsic frequencies is input as a feature vector into a support vector machine optimized by the sparrow search algorithm for training to achieve the identification of broken strands in overhead transmission lines. This paper studies the inherent frequency changes and identification results of overhead transmission lines under two states of normal operation and broken 5 strands operation, and the test results show that the identification method based on VMD-SSA-SVM has good identification effect on broken strands of overhead lines.

Study on modal parameter identification of engineering structures based on nonlinear characteristics

AbstractTo study the nonlinear characteristics of the modal recognition of civil engineering parameters, a method of nonlinear recognition of the parameters of characteristics based on LMD is proposed. The LMD method is applied to decompose the acceleration response signals of the disturbing structure of the building, to obtain the PF components, the instantaneous frequency, and the instantaneous amplitude of each PF component, to determine the modal natural frequency and damping coefficient. To determine the modal parameter based on the LMD, the calculation and analysis results are presented as follows: the frequency of the components fluctuates between the fifth and sixth models, which shows that the components contain the reaction of the fifth and sixth design modes. This is because these two modes (3.101 Hz and 3.147 Hz) are very close to each other, which makes it difficult to distinguish between the responses of these two modes by the LMD method. The frequency of the components is always stable (the first 2.5 s), which indicates that during this period the responses of modes 5e and 6e do not dampen, and the ratio between them in the PF1 components does not differ much. The component frequency curve shows an interesting phenomenon. Starting from about 3.8 s, the frequency curve gradually approaches the first mode, and only the frequency of the first mode is about 6 s, which indicates that the response of the first mode still exists and makes up a significant proportion. Modular response, caused by the damping, is only detected in the first half of the 10 s response, after which it is verified from the nonlinear characteristics of the LMD parameter recognition method that half of the third-order modal response on the scale is very low and almost equal to zero, and despite problems with dense frequency separation mode in the LMD method, the frequency responses of its PF components may reflect the mode combination phenomenon and reflect the duration of each mode throughout the response.

Study on the Damage Diagnosis of Ancient Wood Structure in Tianshui under Traffic Excitation

Three‐dimensional finite element model of the wood structure‐foundation of the North House’s main hall in Tianshui under ground traffic excitation was established, and the damage of the first‐floor longitudinal beam of the wood structure was simulated by the finite element method. The node acceleration response signals on beams were decomposed with a wavelet packet. The wavelet packet energy curvature difference index was proposed to determine the structural damage location. The results show that the wavelet packet energy curvature difference can decide the damage location of the middle part of the first‐floor longitudinal beam, and the index value increases with the increase of damage degree. If SNR is 40 db or larger, the damage index can accurately identify the damage of ancient wooden structures. When SNR increases, the damage index will have an increasingly higher sensitivity and certain ability to resist the effect of noise. The function relation between the damage index and the damage degree is obtained to determine the structural damage degree. The study results provide a theoretical basis for the damage diagnosis of the wood structure of ancient buildings along the traffic line.

Research on the Simulation of Wheelset Response Characteristic Identification of Railway Fastener Loosening

Rail fastener is a crucial component equipment to ensure the safe operation of the train, and it is very paramount to detect the loose state of the fastener. In this paper, the vertical vibration acceleration signal of wheelset is taken as the research object, and the loose state of fastener is identified by separating and calculating the key IMF energy entropy. Firstly, based on the finite element theory and the principle of multibody dynamics, the rigid-flexible coupling simulation model of vehicle track is established. Then, the vertical vibration acceleration signals of the wheelset under the speed of 200 km/h are obtained by setting the different loosening degrees of the fastener. Finally, we use optimized HHT to process signals, and the orthogonal empirical mode decomposition method (OEMD) is proposed to optimize the orthogonality of the intrinsic mode function, to eliminate the IMF component having poor correlation with the original signal; the Hilbert time spectrum and information entropy theory are combined to calculate the energy entropy of the key IMF, and the HHT energy entropy evaluation algorithm of the vertical acceleration response signal of the train wheelset is proposed. The simulation results show that the HHT energy entropy of 100% fastener looseness is less than 25%, 50%, and 75%, decreasing trend. The algorithm can recognize the looseness of track fastener through the experiment under different working conditions.

Model-free damage localization of structures using wavelet based grey relational analysis

Damage detection of real-life civil structures is generally performed under a condition of poor information, mainly referring to our insufficient knowledge of structures, uncertainties and limited sensors. Moreover, most damage detection methods require the numerical or mathematical models of a structure, whose stiffness parameters are sought by inverse solutions such as model updating. However, ill-conditioning problems are often found when updating complex structures. Hence, a model-free damage localization strategy has been proposed incorporating wavelet packet transform with grey relation analysis. First acceleration response signals are decomposed into different frequency subbands using wavelet packet transform. The wavelet packet energies of specific frequency subbands corresponding to fundamental modal frequencies are extracted and then normalized by the total energy of all the accelerometers. After that, wavelet packet energy curvatures are constructed and incorporated with grey relation analysis to propose a wavelet grey correlation coefficient. Damage locations are determined according to magnitude changes in these coefficients. Lastly, the feasibility of the proposed method has been successfully verified against an experimental steel beam. The validation also found that direct damage localization based on mode shapes or modal curvatures could not provide effective judgment.

Localization of damaged cable in a tied‐arch bridge using Arias intensity of seismic acceleration response

It is well-known that a bridge may end up with severe damage under aftershocks, even if it resisted the main shock. As such, a quick assessment seems necessary when a bridge experiences an earthquake event. In this paper, an output-only energy-based method was developed to locate damaged cables in an tied-arch bridge under seismic excitation. For this purpose, seismic acceleration response signals were first subjected to simple band-pass filtering. Then, the energy levels, in terms of Arias intensity, were calculated and normalized to locate the damaged cables. The proposed method was validated through a realistic 3-D numerical model of a tied-arch bridge under 16 different ground motions. It was shown that the proposed method can accurately detect the damaged cables under the earthquakes. Moreover, the damage detection procedure is proved to be insensitive to noise and adequately robust against near- and far-field parameters of the earthquake.

Experimental study of grout defect identification in precast column based on wavelet packet analysis

Grout defects always exist in sleeves of precast structures, but studies on grout defect identification are rarely performed. This article proposes a combination method of dynamic excitation technique and wavelet packet analysis for sleeve defect identification in the precast structure. Hammer excitation on a 1/2-scaled two-floor precast concrete frame structure with column rebar splicing by grout sleeves is conducted to collect column acceleration responses. Moreover, the corresponding energy spectrum is obtained by the wavelet packet analysis. Furthermore, three defect identification indices, that is, percentage of energy transfer, energy ratio variation deviation, and energy spectrum average deviation, are calculated and compared. Robustness analysis of the energy ratio variation deviation is carried out by adding white noise in the original acceleration response signals. The results show that (1) the percentage of energy transfer, the energy ratio variation deviation, and the energy spectrum average deviation are positively correlated with the grout defect degree where the energy ratio variation deviation is more sensitive in the identification of defects; (2) the energy ratio variation deviation robustness of the original signal with the inputted multiplicative white Gaussian noises is better than that with the inputted additive white Gaussian noise; and (3) the proposed defect identification method can characterize the sleeve grout defect degree in column.

Structural health monitoring and assessment using wavelet packet energy spectrum

A novel methodology about structural health monitoring and assessment using the wavelet packet energy spectrum is developed by analyzing the acceleration response signal, aiming to identify the structural damage in real time and perform early structural damage alarming. The structural damage warning will be made according to baseline thresholds, which are obtained from a convergence analysis based on the mean and variance value of two structural damage indicators, namely energy ratio deviation (ED) and energy ratio variance (EV). To illustrate the effectiveness of the method, the operational stage of the Wangzong tunnel in the Wuhan Metro Line 3 in China is taken as a case study. The results demonstrate that (1) The multi-threshold wavelet packet denoising method is effective in removing noise and reserving information within 0.1 Hz; (2) The damage indicators ED and EV from the wavelet packet energy spectrum are sensitive to structural damage, whose mean and variance value will converge well along with the growth of wavelet packet component number; and (3) By the threshold value based on the one-side 98% upper confidence limit, the potential structural damage can be detected and alarmed dynamically in the underground filling with complexity and uncertainty. In conclusion, this research contributes to developing new metrics for structural health evaluation with consideration of time dimension and vibration properties of the structure, which is helpful in detecting and alarming the possible structural damage prior to structural failures. Its inherent limitations lie in the relatively complicated computing process and strict requirements in sensor location.

Experimental study on micro-damage identification in reinforced concrete beam with wavelet packet and DIC method

Abstract Structural damage identification has become a research hotspot in the field of science and engineering. Previous predominant lines of research on structural damage identification methods focused on macroscopic damage, which is visible to naked eyes, but there are few studies on structural micro-damage identification. Towards this end, this paper proposes a combination of digital image correlation (DIC) technique and wavelet packet analysis for micro-damage identification and to achieve real-time early warning of micro-damage initiation in concrete structures. This work implements a four-point bending test of reinforced concrete beams, as well as adopts the step loading and unloading method, following which, the beam acceleration response signal and the damage strain field are collected. Subsequently, the collected response and excitation are processed to obtain the excitation response function, reflecting the dynamic structural state. The wavelet packet is used to process the excitation response function and image under each load level to obtain the corresponding energy spectrum. In this way, the respective micro-damage identification indices are calculated for comparison. The experimental results show that the proposed methodology can properly characterize the damage state of structures under various stages of stress, provide a warning for the initiation of micro-damage, and even accurately determine the location and degree of micro-damage, based on the damage strain field.

Effective Heterogeneous Data Fusion procedure via Kalman filtering

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

Structural Health Monitoring for Multi-story Shear Frames Based on Signal Processing Approach

Non-destructive vibration-based global structural health monitoring and damage detection methods are divided into modal-based and signal-based methods. There is much research in these areas, but each one has some advantages and disadvantages. The combination of these two areas can greatly overcome these disadvantages and thus can enhance the robustness of the damage detection methods. This article presents a signal-modal-based structural health monitoring technique for multi-story shear buildings subjected to an earthquake event. The proposed method utilizes the combination of wavelet transform and FFT as signal processing tools and also Hilbert transform with the aim of final modal parameters extraction, in order to determine the existence, location, and extent of damage in the multi-story shear frames. In fact, extraction of final modal parameters from the acceleration response signals is desired. To demonstrate the capabilities of the proposed algorithm, numerical simulations are performed on a four-story two-bay and ten-story two-bay shear frame with different damage scenarios using OpenSees. The results show that this method can detect the occurrence, location, and severity of the damage with good accuracy even in different levels of multi-damage scenarios and also in the presence of measurement noise.

Full-scale measurements and damping ratio properties of cooling towers with typical heights and configurations

The standard damping ratio for cooling towers is equivalent to that of high-rise reinforced concrete structures, which is 5%. But considering the unique configuration and material properties of the cooling towers, the actual damping ratio of cooling towers is far smaller than the standard value. Damping ratio is an important input parameter for wind and seismic analyses, its value directly determines the anti-wind and anti-seismic safety of the cooling towers. However, few field tests and damping ratio experiments have been conducted for cooling towers at home and abroad. In this paper, we performed field tests of 8 cooling towers with typical height and configuration in the inland areas of China. Acceleration response signals (vibration signals) at typical points of the tower body were obtained under ambient excitation. The measured acceleration signals were preprocessed using the random decrement method (RDM) and natural excitation technique. The natural frequencies and damping ratios of the first ten modes were obtained by combining three modal recognition methods, namely, auto-regressive and moving average (ARMA) model, Ibrahim time domain (ITD), and spare time domain (STD). The measured values were compared against the results of finite element analysis (FEA) and the errors were assessed. Then the equivalent synthetic damping ratios of the 8 cooling towers were estimated based on modal combination. Finally we provided the calculation formulae for the damping ratios and equivalent synthetic damping ratios of the first ten modes by using the fundamental frequency as the objective function and the measured damping ratios of the 8 cooling towers. The measured frequencies were consistent with the results of FEA for the 8 cooling towers, and the maximum difference in fundamental frequency was 4.4%. The damping ratios recognized of each mode showed a dispersed distribution. The maximum damping ratio of the first ten modes was 2.86%. The equivalent synthetic damping ratios of the 8 cooling towers ranged from 1.13% to 2.16%. Error analysis indicated that the fitting formula for the damping ratio had high precision and high stability. The present research provides valuable clues for the determination of damping ratio for large cooling towers and enhances the understanding on the damping mechanism.

Sine phase compensation combining an amplitude phase controller and a discrete feed-forward compensator for electro-hydraulic shaking tables

This article presents a novel control strategy on an electro-hydraulic shaking table under the acceleration control combining an amplitude phase controller and a zero phase error tracking controller with a discrete feed-forward compensator. Because of the electro-hydraulic system’s nonlinearity, phase delay and amplitude attenuation exist in the acceleration response signal inevitably when the electro-hydraulic shaking table system is excited by a sine vibration signal. Moreover, the phase delay of the electro-hydraulic shaking table is composed of phase deviation and actuator delay. For improving the acceleration tracking accuracy, an amplitude phase controller is employed to compensate the phase deviation and amplitude attenuation by introducing weights to adjust the reference signal. Meanwhile, the discrete feed-forward compensator is applied to compensate the actuator delay. As an offline compensator, the zero phase error tracking controller is employed to compensate the phase delay of the response signal and improve the convergence speed of the proposed controller. Overall, the proposed control strategy combines the merits of these three controllers with better tracking performance demonstrated by simulation and experimental results.