Structural Control & Health Monitoring最新文献

As the span of cable-stayed bridges increases, so does the length of stay cables, making cable vortex-induced vibrations (VIVs) more prominent. This is particularly evident in higher-order multimodal VIVs, which are closely linked to the damping characteristics of the cables. Traditional operational modal analysis (OMA) methods often fail under VIV conditions due to the inadequacy of the white noise excitation assumption. Moreover, potential influences from ambient vibrations and noise contamination introduce further uncertainties into the identification results. This paper addresses these challenges by proposing a novel Bayesian method for damping identification from measured VIV responses. The proposed method, based on a single-degree-of-freedom (SDOF) vortex-induced force model and the statistical properties of the power spectral density of the VIV measurements, aims to enhance the accuracy of damping identification while effectively quantifying uncertainties of identified results. The efficacy of the proposed method is validated through simulated scenarios and applied to the field test of a stay cable in the Sutong Bridge. The results not only demonstrate the method’s high accuracy in identifying damping ratios under VIV but also highlight its capability to effectively quantify the uncertainties in the identification results. This method offers a reliable approach for investigating the evolution of damping in VIV of stay cables and enhances the understanding of the mechanisms behind higher-order multimodal VIV.

This study presents a novel approach for anomaly event detection in large-scale civil structures by integrating transfer learning (TL) techniques with extended node strength network analysis based on video data. By leveraging TL with BEiT + UPerNet pretrained models, the method identifies structural Region-of-Uninterest (RoU), such as windows and doors. Following this identification, the extended node strength network uses rich visual information from the video data, concentrating on structural components to detect disturbances in the nonlinearity vector field within these components. The proposed framework provides a comprehensive solution for anomaly detection, achieving high accuracy and reliability in identifying deviations from normal behavior. The approach was validated through two large-scale structural shaking table tests, which included both pronounced shear cracks and tiny cracks. The detection and quantitative analysis results demonstrated the effectiveness and robustness of the method in detecting varying degrees of anomalies in civil structural components. Additionally, the integration of TL techniques improved computational efficiency by approximately 10%, with a positive correlation observed between this efficiency gain and the proportion of structural RoUs in the video. This study advances anomaly detection in large-scale structures, offering a promising approach to enhancing safety and maintenance practices in critical infrastructure.

Considering the critical role of uncertainties in structural damage detection, primarily arising from measurement errors and finite element model discrepancies, a nonprobabilistic approach based on interval analysis is proposed. This nonprobabilistic approach integrates phase space matrices with convolutional neural networks (CNNs) for damage identification. The compatibility of the phase space matrix data format with CNN allows for high sensitivity in detecting damage. Unlike probabilistic methods, this approach does not rely on specific probability distributions but considers the upper and lower bounds of uncertainties, making it highly applicable to real-world applications. The proposed method employs the phase space matrix as the input for the CNN and the elemental stiffness parameter (ESP) as the output. When accounting for uncertainties, distinct networks are developed from the upper and lower bounds of the input phase space matrix. Both the undamaged state and the state under assessment are processed through these networks. The resulting outputs enable the computation of the possibility of damage existence (PoDE) and the damage measure index (DMI), which collectively provide a comprehensive assessment of the level and probability of damage. Validation using a numerical model and experimental data confirms the effectiveness of this method in accurately determining the location and level of damage while considering uncertainties.

Substructural identification has shown privileges compared with direct identification of structures. However, unknown substructural interface forces between adjacent substructures are the key but difficult issues in substructural identification. Current substructural identification methods with full-order substructural models still encounter ill-posed identification problems when there are many unknown substructural interaction forces. Thus, it is necessary to study the identification of substructures with reduced-order substructural boundaries. In addition, current substructural identification based on Kalman filtering still assumes that measurement noises are random Gaussian processes. In this paper, a method is proposed for the identification of substructural damage by reduced-order substructural boundaries and an improved particle filter with unknown input for non-Gaussian measurement noises. First, the boundary degrees of freedom of substructural boundaries together with the number of unknown boundary interaction forces are reduced by the characteristic constraint mode approach. Then, based on the reduced-order model of substructure in modal coordinate, an improved particle filter with unknown inputs is proposed, in which the importance density function and particle generation in particle filtering are established by the unscented Kalman filter with unknown inputs. Finally, substructural damage can be identified without the full observations of acceleration responses at the substructure boundaries. The effectiveness of the proposed method is verified through a numerical substructural damage of a planar frame model.

Full-field dynamic displacement (FFDD) is important for bridge condition assessment. However, it is challenging to monitor the FFDD with high accuracy due to limited sensors and environment variation. This paper proposes a FFDD reconstruction method for bridge based on modal learning. Firstly, the transfer function of dynamic strain response of finite points (SRFP) and FFDD are derived based on the beam bending theory and modal superposition method. Then iterative particle swarm optimization (IPSO) is employed to facilitate self-learning of mode shape with the ability of adapting environment variation. Subsequently, the procedure for reconstructing bridge FFDD by utilizing SRFP and the learned transfer function is provided. Finally, the effectiveness of the proposed method is verified by numerical and experimental examples of bridge under random load, impact load, and moving load excitation, and effects of sensor placement, road roughness, and measurement noise on the reconstruction accuracy are systematically investigated. The results indicate that the proposed method can accurately reconstruct the FFDD in the presence of environment variation, road roughness, and measurement noise at the cost of limited sensors.

Due to the influence of complex service environments, the bridge health monitoring system (BHMS) has to face issues such as sensor failures and power outages of data acquisition systems, leading to frequent occurrences of data missing events including continuous and discrete data missing. By comparison, the continuous data missing can cover up the time-series characteristic and make the corresponding recovery present a greater difficulty, especially for the data with a large loss rate or complicated features. To this end, this paper develops a novel signal recovery method based on the combination of successive variational mode decomposition (SVMD) and TCN–MHA–BiGRU, which is the hybrid of temporal convolutional networks (TCNs), multihead attention (MHA), and bidirectional gated recurrent unit (BiGRU). In this method, SVMD with high reliability and strong robustness is initially employed to decompose the original signal into multiple stable and regular subseries. Then, TCN–MHA–BiGRU incorporating the concept of “extraction-weighting-description of crucial features” is designed for the independent recovery of each subseries, with the ultimate recovery result derived through the linear superposition of all individual recoveries. This method not only can effectively extract the data time-frequency characteristics (e.g., nonstationarity) but also can accurately capture the data time-series characteristics (e.g., linear and nonlinear dependences) within the data. The case study and the subsequent applicability analysis grounded in the monitoring data from BHMS are employed to comprehensively evaluate the effectiveness of the proposed method. The results indicate that this method outperforms compared methods for the recovery of continuous missing data with different missing rates.

Tuned liquid damper (TLD) is one of the main technologies for passive control. The fundamental modal parameters of a TLD contain natural frequency and damping ratio, which are primarily related to the liquid height in the TLD device. Although the liquid height of the TLD is calibrated before installation, it is still necessary to identify the physics parameters of the TLD and the main structure during the service period to prevent the detuning of the TLD. A physics parameter identification method for the structure-damper-coupled system based on mechanics-enhanced stochastic subspace identification (SSI) is proposed in this paper, illustrated through annular TLD (ATLD). By extracting the state matrix of the first controlled mode of the main structure, the natural frequency and damping ratio of the ATLD are identified, thereby determining the liquid height of the ATLD. Numerical models of structure-ATLD-coupled systems with different degrees of freedom are constructed, and their simulated acceleration responses under different excitations are obtained. Sensitivity analysis of environmental noise is performed to verify the accuracy and robustness of the proposed parameter identification method. A dynamic test was designed for a steel chimney model to further verify the practicality of this method. The results show that when the noise level of the measurement noise is below 5.0%, the average relative error in identifying the ATLD liquid height does not exceed 10%. The identification error in the damping ratio of the structure-ATLD-coupled system will lead to a decrease in the accuracy of ATLD liquid height estimation. The proposed method can effectively identify changes in the ATLD liquid height within the optimal frequency ratio range by analyzing the experimental data from the chimney model. The proposed method can effectively estimate the modal parameters of the coupled system, providing reliable data support for evaluating the working condition of the ATLD during its service period.

Laminated rubber bearings may exhibit rupture damage due to factors such as temperature variations and seismic activity, which can reduce their isolation performance. Current detection methods, including human-vision inspection and computer-vision inspection, have certain limitations in accurately assessing the degree of rupture damage. This study attempts to combine the piezoelectric-based active sensing method with a machine learning algorithm to detect rupture damage in laminated rubber bearings. A series of laminated rubber bearings with varying degrees of rupture damage were fabricated, and 1440 sets of detection signals were obtained through experiments using the active sensing method. This study proposes a hybrid machine learning algorithm that integrates a one-dimensional convolutional neural network (1DCNN), long short–term memory (LSTM) network, Bayesian optimization (BO) algorithm, and extreme gradient boosting (XGB) algorithm. The algorithm involves using the 1DCNN and LSTM algorithms to extract the deep features from the wavelet packet energy spectra of the detection signals, and then employing the XGB algorithm optimized by the BO algorithm to construct the prediction model. The research results indicate that the proposed 1DCNN–LSTM–BO–XGB model achieved an accuracy value of 98.6% on the test set, outperforming the 1DCNN–LSTM (91.7%), 1DCNN (88.9%), LSTM (25.0%), XGB (90.3%), and SVM (66.7%) algorithms. Therefore, the combination of the active sensing method and machine learning algorithm shows promising application prospects in detecting the degree of rupture damage in laminated rubber bearings.

Mesoscale models are crucial for the refined analysis of material damage behaviors. However, it remains a challenging task to calibrate a mesoscale model so as to accurately simulate the mechanical behaviors (MBs) of macroscale structural components. The models may be nonlinear, involve numerous material parameters (MPs), and be large-scale. In addition, solutions to inverse problems may lack accuracy or be nonunique. A recent emerging method, physics-informed neural network (PINN), combines deep learning with physical laws to solve complex problems and significantly reduce computational costs. This paper presents an effective PINN approach for mesoscale model calibration. The approach establishes a relationship between the MPs of a mesoscale model and the MBs of structural components using PINN, with constraints based on known physical relationships. Both forward PINN (MPs as inputs and MBs as outputs) and reverse PINN (swapping inputs and outputs) models are used. Calibration is achieved efficiently by combining the forward PINN model with an optimization algorithm or directly using the reverse PINN model. Validation is performed using a mesoscale concrete model in peridynamics (PDs). The relationship between the elastic modulus of bonds in PD and MBs of components is constrained by physical laws. The datasets are generated through OpenSees analysis. The PINN method demonstrates its effectiveness, particularly with the reverse model, which is both efficient and accurate.

Weakened structural stiffness is often a consequence of building damage, particularly after severe events such as earthquakes, where compromised structural performance can pose significant risks. To prevent immediate structural failure, an early warning system is essential, which requires inspection of local components. This research aims to achieve that by exploring the wave propagation analysis method, specifically seismic interferometry. Previous studies have applied this method to building structures, treating them as homogeneous layers of grouped floors. By analyzing the wave travel time along the height of these layers, the fundamental period of the building was estimated. However, this approach did not account for local damage or the variability of structural components, similar to the limitations of vibration-based damage detection methods, which mainly identify global changes. Thus, the goal of this paper is to improve structural health monitoring by examining the sensitivity of wave screening, bridging the gap between nondestructive testing and vibration-based damage detection. A half-scale, seven-story building model, characterized by vertical stiffness irregularity and transverse plan asymmetry, was tested in a laboratory setting. Two vertical sensor arrays were placed near corner columns of different sizes, representing both strong and weak structural areas. These arrays recorded floor accelerations in three directions. The study confirmed the effectiveness of wave propagation analysis for detecting damage along the sensor arrays before and after the earthquake. A transmissibility damage indicator was used to correlate changes in wave velocity, providing a quantitative assessment of damage levels along the wave propagation path.