Structural Control & Health Monitoring最新文献

Continuous monitoring of building displacement is crucial for urban structural safety. While traditional methods are costly, Interferometric Synthetic Aperture Radar (InSAR) offers a cost-effective alternative, providing long-term displacement data. However, due to the insensitivity of SAR radar to north-south displacement, using InSAR alone can only measure settlement and east-west displacement. To address this limitation, this paper presents a three-dimensional (3D) deformation extraction model. The model embeds domain knowledge to introduce additional constraints, which are then used to establish the relationship between north-south and east-west displacement. This relationship allows for the extraction of 3D displacement of buildings from the line of sight (LOS) displacement measured by InSAR. This model was applied to Tower 2 of Yingli International Financial Center (YIFC) in Chongqing, China, and the 3D displacement of the building between 2018 and 2021 was obtained.

The main girders of suspension bridges experience significant deformation due to temperature variations, wind dynamics, and vehicle loads, causing movement at the girder ends and friction among components such as bearings, expansion joints, and viscous dampers. Early warning of the component anomaly is vital for preventive maintenance. This paper develops a two-stage framework for predicting girder end displacement to facilitate anomaly detection. First, a Bayesian neural network is employed to predict girder end cumulative displacement, accounting for uncertainties inherent in the prediction process. Second, an anomaly detection algorithm utilizing a Mahalanobis distance–based approach is implemented to provide warnings to operations based on both measured and predicted data. The effectiveness of the proposed approach is validated using data collected from multiple loads and displacement responses of a suspension bridge. The analysis reveals that the GEV distribution is highly proficient in capturing the underlying pattern of the cumulative displacement indicator, enabling the establishment of an appropriate threshold. This method proves successful in identifying anomalies in critical components such as viscous dampers, enhancing predictive and preventive maintenance practices and contributing to the longevity and safety of bridge infrastructure.

Shake tables are useful earthquake simulation tools for structural seismic experiment, but they may also inadvertently induce vibrations to nearby buildings while in operation. Accelerating the comfort level quantification process of these vibrations before conducting a shake table test is necessary. To this end, this paper focuses on the influence of vibration introduced by a 6 × 9 m large-scale shake table at Southeast University and presents a one-dimensional convolutional neural network–based deep learning approach to efficiently estimate the vibration comfort of the shake table laboratory and surrounding buildings. Based on the on-site structural vibration monitoring of shake table test, a three-dimensional numerical model of the shake table–soil–surrounding building system is established and validated through the finite element method, and thus a dataset comprising 12,215 groups of input (i.e., peak acceleration values and time-history of the triaxial ground motion) and output (i.e., three-directional acceleration response for nine measuring points of surrounding buildings) data is simulated. Thereafter, the deep learning network is trained with 80% of the dataset and tested with the remaining 20%. The test results indicate that the approach enables the network to directly extract dynamic features from triaxial ground motion accelerations and to accurately estimate the weighted acceleration level (WAL) of nine different locations at the surrounding buildings. Finally, the optimized network is verified through an actual shake table experimental test on a self-centering concrete structure, which confirms the superior performance of the proposed approach on shake table–induced vibration comfort estimation. The approach is also beneficial for researchers to design reasonable loading scenarios before conducting shake table tests.

In this paper, evaluation of wind-induced vibration and travel comfort of the long-span suspension bridge were comprehensively conducted based on multisource monitoring data. The wind field distribution and turbulent characteristics during the normal and vortex-induced vibration (VIV) period were comparatively revealed. It reveals that the bridge experienced vertical VIV due to the long-duration wind with specific speed perpendicularly acting on the girder, which cannot be predicted by the turbulence intensity and gust factor. Meanwhile, dynamic response evolution, VIV lock-in effect, modal identification, and wavelet spectrum were further explored based on displacement and acceleration. The VIV frequency was consistent with a natural frequency of the bridge, whose mode can been determined by the deflection correlation heat map. The VIV was due to periodic vortex shedding generate aerodynamic forces, and the reaction of the structure vibration on vortex shedding can cause the vortex shedding frequency to be “locked” over a considerable range of wind speeds. According to driving visual safety and vibration tolerance, it is indicated that such VIV of the bridge may lead to the very discomfort for driving and pedestrian can tolerate short-term vibration in this period. Comfort evaluation for the bridge during the VIV should be further improved accuracy and reliability, which can contribute to emergency response to VIV situations. Note that a certain degree of discomfort may occur under normal vibration conditions, which raises doubts about the reasonableness of the limit value.

The identification of surface damage and structural components is critical for structural health monitoring (SHM) in order to evaluate building safety. Recently, deep neural networks (DNNs)–based approaches have emerged rapidly. However, the existing approaches often encounter catastrophic forgetting when the trained model is used to learn new classes of interest. Conventionally, joint training of the network on both the previous and new data is employed, which is time-consuming and demanding for computation and memory storage. To address this issue, we propose a new approach that integrates two continual learning (CL) algorithms, i.e., elastic weight consolidation (EWC) and learning without forgetting (LwF), denoted as EWCLwF. We also investigate two scenarios for a comprehensive discussion, incrementally learning the classes with similar versus dissimilar data characteristics. Results have demonstrated that EWCLwF requires significantly less training time and data storage compared to joint training, and the average accuracy is enhanced by 0.7%–4.5% compared against other baseline references in both scenarios. Furthermore, our findings reveal that all CL-based approaches benefit from similar data characteristics, while joint training not only fails to benefit but performs even worse, which indicates a scenario that can emphasize the advantage of our proposed approach. The outcome of this study will enhance the long-term monitoring of progressively increasing learning classes in SHM, leading to more efficient usage and management of computing resources.

Identification of damping ratio is very important for the assessment of service performance of long-span bridges. In this paper, an adaptive EKF in modal domain, named adaptive modal EKF (AMEKF), is proposed for identifying the damping ratios of long-span bridges. The dominant modes are selected, and the dimension of the extended state vector is significantly reduced with the aid of modal coordinate and the corresponding modal transformation. Then, the EKF principle is employed for the identification in modal domain. Moreover, an innovation-based procedure is presented to adaptively adjust the covariance matrix of process noise for the purpose of assuring the parametric identification accuracy. A forgetting factor is employed to put proper weights for the previous and current estimates in each time step. A merit of the proposed approach is that all the damping ratios of the selected modes can be simultaneously identified. The effectiveness of the proposed approach is numerically verified via a long-span suspension bridge. The dynamic tests on a simply supported overhanging steel beam and an aeroelastic model of some long-span suspension bridge are further used for the validation. Results show that the proposed approach is capable of identifying damping ratios with acceptable accuracy.

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.