Structural Response Signals Research Articles

A wavelet packet deep learning model for Energy-Based structural collapse assessment under Earthquake-Fire Scenarios: Framework and hybrid simulation

An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy and power spectral density of signals. Moreover, a comparative analysis is performed with 4 previous signal processing models (empirical mode decomposition, variational mode decomposition, wavelet packet transform, and LSTM). Additionally, Latin hypercube sampling is employed to account for uncertainties in structural characteristics and hazards for dataset establishment and fragility analysis. The experimental findings suggest a decline in the structural dominant frequency with the emergence of high-frequency noise and residuals in structural responses due to the multi-hazard effect. The wavelet packet transform eliminates the high-frequency noise in the signal and avoids the oscillation of the LSTM prediction results. Post-earthquake fire increases the structural collapse possibility even under a moderate earthquake excitation. The proposed framework proves to be rational and has the potential to be further applied to other multi-hazard scenarios.

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation.

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

The MLDAR Model: Machine Learning-Based Denoising of Structural Response Signals Generated by Ambient Vibration

Engineers have consistently prioritized the maintenance of structural serviceability and safety. Recent strides in design codes, computational tools, and Structural Health Monitoring (SHM) have sought to address these concerns. On the other hand, the burgeoning application of machine learning (ML) techniques across diverse domains has been noteworthy. This research proposes the combination of ML techniques with SHM to bridge the gap between high-cost and affordable measurement devices. A significant challenge associated with low-cost instruments lies in the heightened noise introduced into recorded data, particularly obscuring structural responses in ambient vibration (AV) measurements. Consequently, the obscured signal within the noise poses challenges for engineers in identifying the eigenfrequencies of structures. This article concentrates on eliminating additive noise, particularly electronic noise stemming from sensor circuitry and components, in AV measurements. The proposed MLDAR (Machine Learning-based Denoising of Ambient Response) model employs a neural network architecture, featuring a denoising autoencoder with convolutional and upsampling layers. The MLDAR model undergoes training using AV response signals from various Single-Degree-of-Freedom (SDOF) oscillators. These SDOFs span the 1–10 Hz frequency band, encompassing low, medium, and high eigenfrequencies, with their accuracy forming an integral part of the model’s evaluation. The results are promising, as AV measurements in an image format after being submitted to the trained model become free of additive noise. This with the aid of upscaling enables the possibility of deriving target eigenfrequencies without altering or deforming of them. Comparisons in various terms, both qualitative and quantitative, such as the mean magnitude-squared coherence, mean phase difference, and Signal-to-Noise Ratio (SNR), showed great performance.

The Accuracy of Frequency Estimation of Structure Vibration under Ambient Excitation: Problems, Causes, and Methods

Accurate identification of building structure frequencies forms the basis for damage detection. The structural dynamic response signal, under ambient excitation, can be transformed into a superposition of multiple single-frequency exponentially damped sinusoids combined with random white noise. However, the peak power spectrum of the response signal tends to exhibit line splitting, compromising the precision of frequency identification. This study examines the accuracy characteristics of the single-frequency free damping vibration signal (SFFDVS) and derives the Cramer–Rao lower bound for the frequency estimator. It thoroughly analyzes the factors influencing the accuracy of SFFDVS frequency identification. The study reveals that the primary cause of spectral line splitting is the random delay inherent in SFFDVS. Based on the maximum likelihood method (MLM), this research introduces the MLM algorithm for SFFDVS and provides a simulation analysis. The findings indicate that the MLM estimation algorithm for frequency parameters effectively addresses spectral line splitting and offers robust noise resistance and recognition accuracy.

Method for Dynamic Load Location Identification Based on FRF Decomposition

The location identification of dynamic load is an important part of load-identification technology. Traditional methods are mostly aimed at the identification of dynamic load’s amplitude and phase. A new method for dynamic load location identification is proposed in this paper. An amplitude ratio or a phase difference between structural dynamic response signals is only related to the frequency response function (FRF), which is a complex-valued function that implies the location information of the load to be identified. In this method, the amplitude and phase variables of the excitation can be eliminated with symbolic calculations, and the location variables can be extracted from the FRF in a complex-number field. An excitation location can be identified quickly with parameter optimization using a Genetic Algorithm (GA), avoiding the ill-posed problem caused by matrix inversion. Numerical simulations and experiments demonstrate that this method can realize the fast recognition of several excitation positions, and has a high recognition accuracy, a short calculation time and a strong anti-noise ability.

Vibro-Acoustic Responses and Pressure Signal Analysis for Early Surge Detection in a Turbocharger Compressor

Abstract High-speed centrifugal compressors may be exploited to pressurize fuel cell systems. Nonetheless, due to fuel cells significant interposed volumes, compressor behavior can lead to severe vibrations related to fluid-dynamic instabilities during part load operating conditions. In particular, surge strongly limits centrifugal compressors stable operating region when moving toward low mass flow rates due to a change in system working point. Therefore, compressor dynamic response must be adequately characterized for early surge detection. To this aim, a dedicated experimental activity was conducted on a vaneless diffuser turbocharger coupled to a solid oxide fuel cell emulator plant; compressor evolution toward surge was investigated. Several signal processing techniques were applied to pressure signals as well as vibro-acoustic responses to better predict compressor behavior and classify its status as stable or unstable. Cepstrum, cross-correlation, and wavelet transform have been identified as suitable techniques to define precursors able to early detect surge. By means of cross-correlation function, propagation phenomena in the ducts can be investigated to assess how they interact near compressor low-mass flowrate unstable conditions. Cepstrum provides a convenient way to determine pressure signal spectrum distortion in terms of further periodic components onset. These harmonic components are due to complex system responses generated by transient phenomena; indeed, cepstrum allows to identify hidden anomalous contributions in system response spectra which may arise in incipient surge conditions. Wavelet transform was performed on both structural and pressure response signals to observe their dominant energy contents temporal evolution; indeed, such spectral pattern time-dependent variation can detect the rise of unstable conditions. By exploiting all these techniques, a complete system identification is performed which allows a deeper investigation of the physical phenomena involved; moreover, a more complete set of surge precursors extracted from different probes' physical signals were defined. The results obtained provide original diagnostic insights for monitoring systems suited to perform early surge detection. Compressor instability prevention can extend its operating range, performance, and reliability to allow better integration with other plant components. Finally, cepstrum application for compressor instability identification can be regarded as a novel method in the fluid machinery field.

Bonding performance monitoring between CFRP and timber column interfaces based on piezoelectric ceramics

To address the problem that carbon fibre reinforced polymer (CFRP) composite material is not easy to directly measure the bond strength of the CFRP-timber column interface during the process of strengthening timber column, a real-time monitoring method of CFRP-timber interface bond strength based on piezoelectric ceramic active sensing is proposed to determine the interface bond strength directly from the structural response signal. A total of 15 specimens were designed for the active monitoring test and tensile test, and the monitoring signals at different enhancement times were compared and analysed using wavelet packets, power spectral density, and short-time Fourier transform, and it was found that the amplitude and power spectral density of the signals increased as the reinforcement time increased. The mapping relationship between wavelet packet energy and bond strength is obtained, providing a more efficient and robust way of monitoring the bond strength between CFRP and timber columns during reinforcement.

Damage detection in plate structures based on baseline construction of ultrasonic Lamb wave using designed mobile transducer set

The ultrasonic Lamb wave-based damage detection method is widely used in plate structures. Due to the multimode characteristic, the collected Lamb wave signals are very complicated, making it difficult to obtain damage information. This paper proposes a damage imaging algorithm based on the baseline construction of the Lamb wave for plate structures. A mobile transducer set including two transducers is designed for Lamb wave excitation and signal collection. To extract damage scattering signals, the response of the signal in healthy areas (the structural response signal) is measured as a temporarily constructed baseline signal. The structural response signal is obtained based on the identified healthy signals collected in different areas of the same structure. Damage scattering signals are extracted as the differences between the current signals and the structural response signal. Damage indexes are calculated based on the damage scattering signals to locate the damage with the imaging algorithm. A damage detection experiment is conducted on an aluminum plate to verify this proposed method. The imaging result is in good agreement with the damage location in the experiment. This paper provides a potential alternative for damage detection of plate structures in practice.

Modal identification of high-rise buildings by combined scheme of improved empirical wavelet transform and Hilbert transform techniques

Recently, the empirical wavelet transform technique has attracted much attention due to its advantage of dealing with non-stationary and nonlinear signals. For empirical wavelet transform-based methods, their performance is heavily dependent on the accuracy of Fourier spectral segmentation. Structural response signals of high-rise buildings under ambient excitations often include high-level noises, which may lead to inaccurate spectral estimation, thereby introducing errors in modal decomposition. To this end, an improved empirical wavelet transform method is presented in this paper to analyze the structural response signals with high-level noises, which employs the projection information of Fourier spectra to improve spectral representation. Then, the improved empirical wavelet transform method is combined with the natural excitation technique and Hilbert transform for identifying modal properties of super-tall buildings under ambient excitations. Through a numerical simulation study, the validity and accuracy of the combined scheme are verified even when structural response signals were embedded with high levels of noise. Furthermore, the proposed method is adopted for modal identification of a 600 m supertall building for evaluating its applicability in field measurements. It is demonstrated that the combined scheme can effectively and accurately evaluate the modal properties of the skyscraper under ambient vibrations with low amplitudes.

Effects of operational variability and damage on structural response signals: A method based on LMS radar image and residual-permutation entropy

The study investigates the residual sequence from time series “black box” models and evaluates the nonlinear and non-stationary characteristics of the residual sequence using Lagrange Multiplier (LM) test. The Lagrange Multiplier Statistics (LMS) radar map is performed to analyze and clarify the corresponding relationship between the residual sequence and structural safety condition. Then, the permutation entropy is proposed to measure the complexity, randomness and fluctuation of the residual sequence, and the correlation is established between the residual-permutation entropy (R-PE), structural abnormal response signal and safety state. The effects of operational variability, damage, coupling disturbance of operational variability and damage on R-PE are analyzed in detail.In this paper, the proposed methodology is verified by vibration response signals of a three-storey shear-type frame in experiment and a six-storey shear-type frame in numerical simulation. The results indicate that i) LMS = 0.9n can be considered as the classification threshold for undamaged and damaged state of the structure using LMS radar map. ii) When the R-PE value is close to 1, it indicates that the structure is undamaged or not influenced by operational variability. iii) When the R-PE value is less than 1, it may be disturbed by operational factors, damage factors, or coupling disturbance of the two. iv) The R-PE value is very robust as the decrease of the R-PE value caused by operational disturbance is small, while the decrease of R-PE value caused by damage is significant. v) There is a significant difference of the R-PE value between small damage and operational impact on the structure.

A novel excitation signal generation technology for accelerated random vibration fatigue testing based on the law of kurtosis transmission

Based on the principle of signal decomposition, the law of kurtosis transmission between non-Gaussian excitation signal and structural response signal is studied, and the inherent relationship between the kurtosis of decomposed signal of excitation and response is found. On the basis of this relationship, a novel excitation signal generation technology for accelerated random vibration fatigue testing is proposed, which has been verified by simulation and experiment. The results show that excitation signal generated by this method can obtain the required high kurtosis response signal for accelerated random vibration fatigue testing, which can effectively reduce the time of accelerated life testing.

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

Recently, structural health monitoring (SHM) methods for civil structures have been investigated widely, especially Deep Learning (DL)-based methods. However, it is usually difficult to fully train a deep neural network, and thus, typical DL-based SHM methods are limited in terms of performance. While addressing these issues, in this paper, a novel methodology is proposed for smart damage identification of frame structures. The newly proposed SHM method is based on raw time-domain structural response signals and deep residual network (DRN). The introduced DRN algorithm has been designed and tested in an effective way for extracting and learning the optimum features of the 1D raw ambient vibration acceleration signals, without any need for engineered features. Also, the network’s performance has been optimized using Bayesian optimization, which clearly enhances the network’s accuracy and information flow across it. Next, the outputs of DRNs are further utilized through new methods for damage size estimation and damage localization. The proposed methodology has been evaluated using the datasets of numerical and experimental frames of the SHM benchmark problem and the dataset of a real-world full-scale truss bridge. The results show that the proposed method is capable of detecting, localizing, and quantifying structural damage accurately for all of the simulated cases of the two examples. Furthermore, conducted comparison studies have approved that the new approach is more efficient than other machine learning-based methods, and it can overcome the major limitations of Artificial intelligence-based SHM models.

Aging state detection of viscoelastic sandwich structure using redundant second generation wavelet packet transform and fuzzy support vector data description

Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.

Bridge safety monitoring and evaluation based on hesitant fuzzy set

This paper reviews the safety monitoring systems, bridge safety assessment methods, and hesitant fuzzy set theories at home and abroad, and presents a novel bridge safety monitoring and evaluation method based on hesitant fuzzy set, which has greatly significance to scientific research. Through multi-scale estimation and Butter Worth (BWorth) filtering, the structural response signals were identified and extracted under car load and crowd load. Then, a mathematical model was established for bridge safety monitoring and evaluation based on hesitant fuzzy set. Drawing on hesitant fuzzy similarity theory, the authors designed damage recognition algorithms based on single index, dual-index linkage, and multi-index linkage. Finally, example analysis was conducted to confirm the feasibility of multi-index linkage, coupled with multi-period dynamic recognition, in the evaluation of bridge structural damages.

Research on Feature Extracted Method for Flutter Test Based on EMD and CNN

Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.

Low energy impact damage identification method of CFRP structure based on wavelet transform and probabilistic neural network

Low energy impact damage identification of carbon fiber reinforced plastics (CFRP) structure is of great significance to ensure the safe service of the structure. In this paper, the low energy impact damage identification method of CFRP structure by using fiber Bragg grating (FBG) sensor, continuous wavelet transform and probabilistic neural network (PNN) was studied. Firstly, the CFRP structure with low energy impact damage was actively excited, and the structural dynamic response signals were accurately detected by FBG sensors. Then, the dynamic response signals were subjected to continuous wavelet transform, and their wavelet time-frequency diagrams were extracted as the structural damage feature. On this basis, with damage feature as input and low energy impact damage as output, the damage identification model based on PNN was established. Finally, the experimental system was constructed to verify the feasibility of the method proposed in this paper. The results showed that for 12 groups of test samples, the damage identification model based on PNN can correctly identified 11 groups. This paper provided a feasible method for the low energy impact damage identification of CFRP structure.

Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.

A Combined Method for Time-Varying Parameter Identification Based on Variational Mode Decomposition and Generalized Morse Wavelet

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

Online Intelligent Identification of Modal Parameters for Large Cable-Stayed Bridges

Realizing online intelligent identification of bridge modal parameters requires not only the adaptive decomposition of structural response signals but also the enforcement of the automatic identification of modal parameters. Therefore, in this paper, the signal decomposition algorithm-ensemble empirical mode decomposition algorithm (EEMD) is improved to fulfill the above task. First, the adaptive matching algorithm is introduced to deal with the endpoint effect; second, the method of classification is used to avoid modal aliasing. Finally, an index for filtering the effective intrinsic mode function (IMF) components is constructed to realize automatic screening and signal reconstruction of the effective IMF components. At the same time, the first derivative of the singular entropy increment is used to automatically determine the order of the system, and then the spectral clustering algorithm is combined with the stochastic subspace algorithm to ultimately reach the goal of automatic identification of modal parameters.

Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam. This nonlinear system identification method is based on identifying the multi-scale dynamics of the underlying system. Only structural dynamic response signals are needed to construct a reduced-order model to represent the system concerned. It makes the method easy to use in practice. A new bolt loosening identification procedure based on the constructed system nonlinear reduced-order model is proposed. A new damage feature to indicate bolt loosening is presented. Experimental works are carried out to validate the proposed method. The results show that the proposed damage detection method can detect bolt loosening effectively, and the proposed damage feature values increase with the increase of bolt torques. The damage feature calculated from the response solution of the reduced-order model can give robust and sensitive indication of bolt loosening.