Measured Acceleration Responses Research Articles

A novel damage identification method based on acceleration response sensitivity and finite element model updating: Numerical and experimental

The utilization of time-domain data for structural damage diagnosis and finite element model updating has several benefits, such as direct implementation of the measured data with no requirement for domain changing and susceptibility to structural damages. This study introduces a straightforward sensitivity relation for the measured acceleration response, avoiding the requirement for computational techniques like direct differentiation methods. The proposed equation establishes a linear relationship between variations of the structural parameters and structural responses. The proposed sensitivity equation operates at the level of improved equations and exhibits greater accuracy compared to the current approaches. The numerical evaluation of the proposed sensitivity equation on two offshore jacket platforms reveals the accuracy of the proposed method. Additionally, an experimental case study to examine the efficacy and validation of the introduced sensitivity relation in real-world settings demonstrates the procedure’s viability. The numerical and experimental results reveal the accuracy of the proposed sensitivity equation.

Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation

This paper aims to assess the nonlinear vibration of a radial gate induced by flood discharge; the measured acceleration response data of a spillway radial gate are analyzed using the chaos theory. The results show that the vibration responses of the gate at three opening heights present clear chaotic characteristics, and the chaotic characteristics of the lower main beam point are greater than other points. Moreover, the y-direction (vertical) correlation dimensions of the three measuring points on the supporting arm are larger than those of the x-direction (axial) and z-direction (lateral). The vertical vibration of the supporting arm is more complex and presents more uncertainties, which should be paid attention to in the literature. Under three different gate opening heights, the maximum Lyapunov exponent of each measuring point ranges from 0.0246 to 0.0681. In addition, the flow fluctuation load is the main excitation source of the gate vibration chaotic characteristics.

Machine learning-aided damage identification of mock-up spent nuclear fuel assemblies in a sealed dry storage canister

Spent nuclear fuel (SNF) assemblies (FAs) contain high-level radioactive waste from operation of nuclear power plants (NPPs). Their safe storage in dry casks is critical for nuclear waste management. However, interim storage or transportation of a canister in a cask, the FAs may be damaged due to human errors, extreme events, or long-term degradation. Therefore, non-destructive evaluation (NDE) methods are needed to monitor their structural integrity over time. In this paper, a novel machine learning (ML)-aided method is proposed to identify different levels of FA damage using measurements taken on the exterior surface of a 2/3-scale experimental canister mock-up. The frequency response functions (FRFs) of the fully loaded canister basket (FLCB) system and the canister with interior damage were computed from recorded input excitation and acceleration response measurements. Machine learning classifiers, including Random Forest (RF), Artificial Neural Networks (ANNs), and Gaussian Naive Bayes (GNB), were trained and tested using features extracted from the FRF difference between an intact FLCB and a damaged canister. The results indicated that the RF model produces the best performance with a testing accuracy of 0.916 and Macro-F1 score of 0.920. Notably, the RF model accurately predicted the damage levels regardless of sensor (i.e., accelerometer) locations. The GNB, however, provided a compromised performance due to its inability to handle correlated features and non-ideal Gaussian distribution of the data. The ANN model exhibited an intermediate performance between the RF and GNB models. Besides, ANN and GNB models had a higher rate of false negatives than the RF models. The accuracy of the model needs to be considered in a real-life implementation because false positives are likely to result in added costs in terms of further inspection while false negatives could potentially result in further degradation and failure.

Development and experimental verification of an IoT sensing system for drive-by bridge health monitoring

Vehicles equipped with various types of sensors have the great potentials to effectively evaluate the health conditions of a population of bridges at a low cost. However, existing drive-by structural health monitoring (SHM) methods acquire vehicle vibration responses offline and export them to a computer for postprocessing. Furthermore, the vehicle trajectory information on the bridge is important for scaling up the drive-by SHM for in situ applications, which is not synchronously measured by existing systems. Therefore, a single-board computer-based IoT sensing system for continuous and real-time drive-by bridge health monitoring is developed in this study. The developed IoT sensing system integrates a triaxial microelectromechanical system (MEMS) accelerometer, temperature sensor, GPS and 4G module on Raspberry Pi 4 Model B. The sensor node can be mounted on a moving vehicle to collect the triaxial acceleration responses, temperature and GPS information. A graphical user interface (GUI) is developed based on the Python Tkinter package to remotely control the sensor node and visualise the collected data in real time. The fast Fourier transform of the measured acceleration responses is performed on the sensor node inboard processor. The raw data are sent to both the cloud server and remote terminal computer through a 4G module. The goal is to provide a low-cost, accurate and scalable sensing system for easy implementation of drive-by bridge health monitoring. The system architecture and workflow of the developed IoT sensing system are presented in detail. A series of experimental tests are conducted to validate the accuracy of the measured acceleration responses and feasibility of using the developed IoT sensing system for drive-by SHM applications.

Shaking table tests and numerical studies on the seismic behaviors of FPB in railway continuous beam bridges

Friction pendulum bearings (FPBs) were extensively used in recent years in railway beam bridges located in the earthquake-prone regions of China. While the efficiency of FPB and reliability of constitutive models have been demonstrated by many experimental studies, the seismic behaviors of FPB in continuous beam bridges in real earthquakes have rarely been investigated. Therefore, a 1/10-scale three-span railway beam bridge isolated by FPB was tested utilizing a 10.1 m × 8.1 m shaking table with six DOFs. The seismic responses of the bridge were analyzed in terms of phenomena, displacement, acceleration, and strain. Different sliding behaviors of the FPB at each pier cap appear because of the interaction with different damaged mid piers. Obvious uplift of the girder ends occurs due to the spherical surface of the FPB, which is evidenced by the measured vertical displacement of the bearings. Numerical simulation based on OpenSees indicates that the FPB element with the suggested friction coefficient model can well reproduce the peak and residual displacements of the FPB. A comparison of calculated and measured acceleration responses reveals that both damaged piers and FPB can filter out the higher frequencies of the input motion. The seismic responses of bridges isolated by the FPB and flat sliding bearing (FSB) were presented based on the validated model under PGAs from 0.2 g to 1.5 g. The FPB dissipates more earthquake energy with small peak and residual displacements, embodying better performance than that of the FSB due to the restoring capacity.

Frequency domain identification of modal characteristics and loads from output‐only measurements

AbstractThis paper proposes a frequency domain framework for concurrently estimating the modal structural parameters and modal loads from the measured output response. A mathematical load model is theoretically developed and compared to the load determined by force identification. Unlike conventional modal identification schemes, the proposed approach can effectively accommodate the spectral shape of the external load in the frequency domain, enabling the determination of the modal parameters without explicit white noise assumption. The proposed approach is verified by applying it to a single‐degree‐of‐freedom system system representing a modal response, an aeroelastic model in an atmospheric boundary layer wind tunnel, a multi‐degree‐of‐freedom system, and the measured acceleration response of a 40‐story building. The simulation results demonstrate that the proposed technique can reliably estimate the modal parameters and load model and is robust against the noise present in the response and the order of the load model. Additionally, the proposed technique can be applied to various loading conditions such as vortex‐induced and buffeting effects since the modal parameters and the load model can be identified for any shape of the loading spectrum.

Investigation of dynamic properties of tall buildings under super typhoon Mangkhut via Bayesian method

Investigations into the variation law of modal parameters of tall buildings under typhoon events via field measurements have been performed extensively in recent decades. In this study, the dynamic properties of tall buildings under super typhoon Mangkhut are investigated via Bayesian operational modal analysis (BAYOMA), which provides the most probable value and identification uncertainty. First, numerical examples are presented to illustrate the excellent performance of BAYOMA in identifying time-varying structural dynamic properties under colored noise excitations. Subsequently, BAYOMA is performed to identify the modal parameters of two tall buildings with different configurations, namely, the Zhuoyue Century Center (280 m) and Guangzhou West Tower (432 m), using measured acceleration responses atop the buildings under significant structural vibration amplitudes (maximum accelerations of approximately 20 mill-g) during Mangkhut. Variations in the natural frequencies and damping ratios with respect to time and amplitude are investigated and discussed. The identified natural frequencies vary significantly with time. An investigation into the identified natural frequencies vs. the modal root-mean-square shows a negative correlation. Meanwhile, the damping ratios show a large dispersion but remain in the same order of magnitude and do not exhibit significant time-varying and amplitude-dependent features. The aim of this study is to provide further understanding into the in-situ dynamic properties of tall buildings and provide an important reference for wind-resistant design and wind-induced vibration control under severe typhoon conditions.

Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary.

How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerometers, this paper proposes a novel time-varying external force identification method using group sparse regularization based on the prior knowledge in the redundant dictionary. Firstly, the relationship between time-varying external force and acceleration responses is established, and a redundant dictionary is designed to create a sparse expression of external force. Then, the relevance of atoms in the redundant dictionary is revealed, and this prior knowledge is used to determine the group structures of atoms. As a result, a force identification governing equation is formulated, and the group sparse regularization is reasonably introduced to ensure the accuracy of the identified results. The contribution of this paper is that the group structures of atoms are reasonably determined based on prior knowledge, and the complexity in the process for identifying external force from measured acceleration responses is reduced. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations and an experimental structure. The illustrated results show that, compared with the force identification method based on the standard l1-norm regularization, the proposed method can further improve the identified accuracy of unknown external force and greatly enhance the computational efficiency for the force identification problem.

Pattern Matching-Based Structural Damage Identification Using Mode Shape Difference Ratio with Limited Sensors

In order to rapidly identify structural damage, a pattern matching-based structural damage identification using mode shape difference ratio with limited sensors is proposed. The mode shape difference ratio is proposed as the damage indicator for pattern matching. The natural frequencies and mode shapes of the structure under different damage scenarios are obtained by the numerical simulation with finite element software. Using the simulation results, the damage pattern library based on mode shape difference ratio matrix is constructed. Afterwards, for the real structure to be test, the response power spectrum ratio is calculated with the measured acceleration responses. Then the mode shape difference ratio matrix of real structure is constructed using the power spectrum ratio of the measured responses with limited sensors. With the reciprocal of correlation coefficient difference (ROCCD) used as the pattern matching approach, the actual structural damage pattern is matched with the pattern in the damage pattern library. The pattern in the library corresponding to the maximum value of ROCCD is considered to be the damage condition of the structure. Finally, the numerical simulation and experiment of a four-layer interlayer shear model are investigated. The results show that the proposed method well determines the damage location and severity of the structure, and possesses excellent practicability.

Structural damage detection with canonical correlation analysis using limited sensors

• Use of small amount of sensor information with structural correlation. • using a combination of canonical correlation analysis and a small number of sensors. • Determining structural state by analyzing structural correlation. • Limited sensors correlation state analysis to identify damage. This paper presents a novel structural damage detection method using limited sensors for beam bridge subjected to a moving mass. In this method, the measured acceleration responses are cut out by a moving window with a length determined by the sampling frequency and the fundamental frequency of the bridge. The windowed responses are processed by the subspace analysis to extract the principal components. The correlation coefficients between the principal components in the window are obtained using the canonical correlation analysis (CCA). Finally, a new damage factor in the window is defined based on the output of the CCA. Moving the window, a time series of the damage factor is obtained. When the moving mass passes through the damage location of the bridge, the curve of the damage factor has a peak that the damage can be identified. To demonstrate the proposed method, a beam bridge is numerically modelled and a simply supported beam bridge model is tested in the lab. Both the numerical and experimental results demonstrate that this method can successfully identify the single and multiple damages with a limited number of sensors.

Acceleration Based Condition Monitoring of Steel Bridges

This paper presents a frequency domain-based method for continuous health monitoring of steel truss bridge, based on the power spectral density and frequency analysis of the recorded acceleration response time histories at the selected node points of a bridge under regular operational conditions. This method is also implemented on a railway steel truss bridge connecting the mainland India to Rameswaram, named the Pamban bridge. The measured acceleration response time histories at some selected nodes on the bridge were parameterized into 4 power spectral density and frequency-based parameters. Also, the frequency analysis of the acceleration response time histories was done. The continuous variation of these parameters with measurement points was observed for about a year to investigate the suitability of these parameters to detect, locate damages in a railway steel truss bridge, and find their severity. Then, the consistency of damages detected by the variation of these parameters and the damages expected based on strain measurements and visual inspections made on the bridge was examined to evaluate the suitability of these parameters for the level 3 damage detection. The same is documented and performance of the parameters discussed.

Monitoring abnormal vibration and structural health conditions of an in-service structure from its SHM data

Vortex-induced vibration occurred recently in several important engineering structures in China. Very large mono-frequency periodic vibration was noted. This study reports on the analysis of recorded data from the structural health monitoring (SHM) system of a suspension bridge with the aim to develop some indicators that may be useful to evaluate if there is any abnormal vibration or change of health conditions of a structure in future. The power spectrum of measured acceleration responses is plotted in three-dimensions of time, frequency and power. Acceleration responses from 16 sensors on the target bridge deck in 233 days are analyzed. The evolution of different dynamic parameters in the period encompassing the occurrences of large vortex-induced vibration are reported. The changes in these parameters are identified to be closely associated with some temporary installations on the deck and temporary measures for vibration suppression. The changes in vibration power at different natural frequencies of the structure can be used to demonstrate the evolution of the unexpected event. The trend and abrupt changes of natural frequencies and modal components show the flow of vibration energy between different vibration modes. In addition to the intuitive three-dimension power spectral plot, two state vectors are also developed from the power spectrum. The identification of abnormal vibration and structural state can then be carried out automatically with the novelty detection method and a threshold. The analysis of the SHM acceleration responses of the bridge deck demonstrates the implementation procedure and the effectiveness of the proposed method.

A semi-analytical method for moving force identification of bridge structures based on the discrete cosine transform and FEM

A semi-analytical method is proposed for moving force identification (MFI) based on the finite element model of the bridge structures. The moving force can be expanded by a set of cosine basis functions based on the discrete cosine transform (DCT) in the time domain. By constructing the segmented continuous mode shape of the bridge deck, the analytical relationship between the measured acceleration responses and the basis coefficients is derived using the Duhamel integral, which is not affected by the time step. The weighted l1-norm regularization method is used to solve the basis coefficients to improve the accuracy and noise immunity for identification. For complex bridge structures, the relationship between the responses and the moving forces can be analytically characterized by only knowing node modes of the bridge deck elements. Thus the identification can be carried out effectively. In numerical simulations, the effects of measured locations, noise levels, mode numbers, and moving speeds on the recognition accuracy are discussed under different models. The effectiveness, applicability, and robustness of the proposed method are verified.

Lost data neural semantic recovery framework for structural health monitoring based on deep learning

AbstractStructural condition perception is a crucial step in structural health monitoring (SHM). Random loss or corruption of sensing data seriously hinders the reliability of the monitoring system. This paper discusses the recovery of randomly lost data in SHM from the perspective of conditional probability generation. A novel data‐driven neural semantic recovery framework is proposed, transforming data recovery into a conditional probability modeling problem. This framework uses deep fully convolutional neural networks with an encoder–decoder architecture to capture the overall semantic features of the vibration data, allowing accurate modeling of the behavior of complex conditional probability distributions. Advanced techniques such as dense connections, skip connections, and residual connections significantly improved the network's parameter utilization and recovery performance. Moreover, a novel perceptual loss function is proposed, enabling the network to integrate data loss patterns effectively. The proposed network can be trained end‐to‐end in a self‐supervised manner and perform efficient inferences. Based on the long‐term measured acceleration response under the ambient excitation of a pedestrian bridge, the recovery performance and robustness of the model are sufficiently verified and evaluated. The network exhibits excellent recovery accuracy and robustness, even if the loss ratio is as high as 90%. Preliminary evaluation results show that the proposed model can be seamlessly transferred to scenarios with continuous data loss without retraining the network. Finally, the application prospects of the framework in modal identification and anomaly monitoring of structural conditions are demonstrated.

Time-Varying System Identification of Precast Segmental Columns Subjected to Seismic Excitations

Precast segmental columns, as one structures, have attracted increasing attention in bridge and building construction. To understand the performance and dynamic characteristics of precast segmental columns subjected to earthquake excitations, this paper develops a novel approach for time-varying system stiffness identification of precast segmental columns under seismic excitations, based on the adaptive unscented Kalman filtering (AUKF) method. In the proposed approach, a precast segmental column model is considered as an equivalent single-degree-of-freedom (SDOF) system with time-varying system parameters. The AUKF method is employed to identify the time-varying stiffness parameters by using measured acceleration responses and then calculate dynamic displacement responses of the SDOF system to verify the accuracy of the identified stiffness parameters. Since the system parameter identification process and measurement noise covariance matrices are updated at each time step, the estimated results can be reliably obtained to evaluate the conditions of segmental columns subjected to seismic excitations. To verify the feasibility and effectiveness of using the proposed approach for time-varying system stiffness identification of segmental columns, numerical studies on a planar precast segmental column subjected to different intensities of ground motion excitations are conducted. Shake table tests on a scaled precast segmental column and a scaled monolithic column subjected to earthquake excitations are conducted in the laboratory to further verify the performance of the proposed approach. Measured acceleration responses are used for time-varying system identification. Both numerical and experimental results demonstrate that the proposed approach can successfully identify the time-varying stiffness values of precast segmental columns subjected to earthquake excitations, and the displacement responses of these columns are predicted accurately as compared with the measured ones.

Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements.

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

Using Two Sensors for Damage Detection of Bridge Based on Transfer Entropy of Vehicle-bridge Information

An innovative damage identification method using only two sensors for damage detection of bridge based on transfer entropy of vehicle- bridge information is proposed in this article. In this method, a test vehicle passing through the bridge surface, and structural vibration acceleration responses at two measuring points are measured from two acceleration sensors, which one set on the bridge, and the other one set on the test vehicle. A fixed moving window is defined as a definitive length calculated by the sampling frequency and the fundamental frequency of the measured acceleration responses. The windowed pair time series extracted from the two measured acceleration responses that obtained from the bridge and the moving vehicle respectively are used to calculate the transfer entropy, which is used to define the new damage index. A simply supported beam bridge subjected to a test vehicle is simulated to verify the effectiveness and feasibility of this method. Numerical simulation results show that this method can accurately detect the damages under both single damage scenarios and multiple damage scenarios using acceleration responses obtained from bridge and moving vehicle. It indicates that the method proposed in this article has a promising application prospect in the field of bridge damage detection.

Displacement response estimation based on measurement of acceleration response and nonlinear hysteresis model

Displacement response estimation based on measurement of acceleration response and nonlinear hysteresis model

Displacement Estimation for Nonlinear Structures Using Seismic Acceleration Response Data

ABSTRACT The displacements estimated using seismic accelerations are useful for post-earthquake inspection of structural damage. In previous studies, displacements were usually obtained through non-trivial measurement or double integration of the measured acceleration response and then an additional correction process. However, from the signal processing perspective, double integration induces substantial numerical errors. In this study, a displacement estimation approach is developed based on the extended Kalman filter (EKF) with an embedded Bayesian noise-parameters updating technique. The displacement is estimated by EKF with updated time-varying noise parameters based on a Bouc–Wen model. Numerical simulations and a shaking‐table test are presented to demonstrate the proposed method.

An approach to investigating rolling element bearing damage under impact loading

This paper presents an approach to studying rolling element bearing damage under the interference of impact loading. In the experimental part, a series of bearing tests was performed by using the twin-disc test device with artificially damaged bearings. This was followed by analysis of the measured acceleration response data in impact-free condition as well as under the influence of the impact loading. The results showed successful detection of the bearing outer race damage by using typical bearing damage detection approaches regardless whether the impact loading was applied to the system or not. In turn, recognition of the bearing rolling element damage required specific signal processing.