Structural Control & Health Monitoring最新文献

The existing scholarly investigations into intelligent structural damage recognition predominantly emphasize the enhancement of the precision and efficacy of damage detection. Nonetheless, the opaque “black box” characteristic inherent to deep learning frameworks constrains users’ comprehension of the underlying decision-making mechanisms, which significantly obstructs their practical progression and execution. Consequently, this manuscript employs the interpretative framework known as Shapley Additive exPlanation (SHAP) to elucidate and scrutinize the attributes of a convolutional neural network–based intelligent structural damage recognition model, while also proposing a methodology for the optimization of features pertinent to structural damage recognition. In particular, this inquiry clarifies the foundational principles that govern the output results of damage assessment and identifies the prospective optimal characteristics of structural damage identification signals. In assessing the contribution of various features to the results of damage recognition and the interrelations among these features, both global and local perspectives of the damage signal were taken into account. The interpretation and analysis of damage recognition signal characteristics can facilitate the selection of structural damage recognition features, thereby aiding deep learning models in the extraction of high-dimensional features and markedly enhancing the recognition accuracy of structural damage identification. The efficacy of the proposed algorithm was corroborated through two experimental scenarios, with results indicating that the accuracy of the structural damage identification algorithm delineated in this study surpassed 95%. This research offers thorough guidance for the implementation of SHAP analysis within intelligent structural damage models, and the findings hold significant implications for augmenting the interpretability of intelligent damage identification algorithms.

Straddle-type monorail systems (STMSs) are increasingly adopted as medium-capacity transit solutions to alleviate urban traffic congestion. However, their operational resilience during earthquakes is challenged by the low lateral stiffness of track beams and single-column piers, with critical components like bearings and piers vulnerable to elastoplastic behavior under seismic loads. To this end, this study proposes a MATLAB + OpenSees co-simulation framework to investigate nonlinear vehicle–bridge interaction (VBI) dynamics in STMSs subjected to seismic excitations. Validation shows high consistency with literature results, with Pearson correlations of > 0.81 and > 0.98 for bridge and train responses and relative errors of maximal values < 4%. Three types of bridge bearings—regular spherical steel bearing, lead rubber bearing (LRB), and friction pendulum bearing (FPS)—are compared to assess their influence on mitigating vibration responses. Both LRB and FPS effectively reduce lateral vibrations of the track beam and train, with maximum reduction rates reaching up to 60%. The shear forces and bending moments at the bottom of the piers are also substantially reduced by the isolation bearings, with reduction rates up to 50%. The proposed approach can be extended for nonlinear VBI analysis of STMSs under severe nonlinear excitations such as strong earthquakes, high winds, or collision loads.

When identifying damage to an offshore wind turbine (OWT) support structure, the influence of harmonic components in vibration response and the difficulty of acquiring data in the damaged state will be encountered. Therefore, the current paper employs the variational mode decomposition (VMD) and sim-to-real deep transfer learning (TL) to identify the damage to an OWT support structure. To eliminate the effect of harmonic components, the vibration response is decomposed using VMD, and the modal response’s reconstructed signal (only containing the structure’s natural frequency) is selected for damage identification. The numerical simulation data and the model test’s measured data are utilized as the source domain (SD) and target domain (TD), respectively. The source model is established by training a convolutional neural network (CNN) with the SD data. The source model’s network structure and weight are frozen to the TD network’s corresponding position. The measured data are utilized to fine-tune the parameters to establish a target model, which is tested to attain the damage identification outcomes. The presented method is validated using the model test data of an OWT support structure.

In this paper, a flutter prediction method based on flutter margins is proposed for streamlined bridges whose aeroelastic behavior can be well approximated by a two-degree-of-freedom (2-DoF) flutter model. The method enables prediction of the flutter boundary by extrapolating a curve of flutter margin versus wind speed, constructed using flutter margins at several subcritical wind speeds. In addition, an $$-optimal flutter active control method based on measured receptances and flutter margins is proposed. It enables assignment of the flutter boundary to a prescribed wind speed value or range for each considered angle of attack (AoA), while simultaneously minimizing vibration responses at subcritical wind speeds, using a single controller with optimal control effort. Hence, the designed controller is robust to the variations of wind speed and AoA. The proposed flutter prediction and control methods require only a small number of systems’ modal parameters or open-loop receptances at several subcritical wind speeds. The proposed flutter prediction method typically requires fewer system modal parameters than existing methods that track the variation of damping ratio against wind speed. The proposed flutter suppression method avoids some modeling errors associated with conventional system matrix-based methods. The working of the proposed flutter prediction and control methods are validated using wind tunnel tests and CFD simulations, respectively.

In tunnel structural health monitoring (SHM) systems, data completeness and accuracy are essential for tasks such as damage detection and early warning. However, environmental disturbances and sensor faults often cause significant missing data, making effective imputation a critical preprocessing step. Traditional statistical methods struggle to capture complex nonlinear temporal and cross-feature dependencies, while autoregressive models, such as recurrent neural networks, suffer from error accumulation and difficulty adapting to dynamically varying strain distributions in real tunnels. To address these challenges, this work proposes a novel nonautoregressive imputation framework based on diffusion models, which effectively mitigate error accumulation. The model effectively exploits the informative content of observed data to guide the modeling and reconstruction of missing values. A gated temporal-feature self-attention fusion module is introduced to accurately capture the complex temporal and spatial dependencies of structural responses. Additionally, external environmental variables such as temperature and water level are integrated to jointly model structural responses and operating conditions, ensuring that the imputation remains robust even under harsh environmental conditions. The method is validated on two real-world SHM datasets from tunnels in Nanjing and Wuhan with various missing data patterns. Experimental results show consistently robust and superior performance across different missing rates, maintaining high accuracy even under severe data loss, demonstrating its effectiveness and practical value in real SHM applications.

Due to their high flexibility and low damping, long-span suspension bridges are susceptible to multimodal vortex-induced vibration (MVIV) under low and normal wind speeds. However, it remains a challenge for the currently used control strategies to achieve optimal additional damping ratios for different modes of vibration as wind speed varies. To this end, this study presents a multimodal control strategy for mitigating MVIV of long-span suspension bridges using magnetorheological (MR) dampers. A vortex-induced force (VIF) model is first established based on the VIFs identified from the wind and structural responses of a bridge during MVIV measured on site. The MVIV of the bridge is then simulated by applying the VIF model to the finite element model of the bridge, and the optimized setup of the control system, consisting of MR dampers and supporting brackets, is sought in terms of a passive control strategy. The multimodal control strategy, which is a novel semiactive control strategy, is finally proposed based on the self-excited characteristics of MVIV observed on site and a linear quadratic regulator. To demonstrate the effectiveness and robustness of the proposed control strategy, a real long-span suspension bridge once suffering MVIV is chosen as a case study. The results demonstrate that the proposed control strategy can robustly mitigate the MVIV of the bridge in the first fourteen modes of vibration in vertical direction, and the effectiveness of the proposed strategy is superior to passive or other semiactive control strategies.

This study investigates the effectiveness of different monitoring strategies for estimating bridge displacement trends induced by landslides, with a focus on addressing three key questions: (i) Can bridge displacement trends induced by a landslide be monitored using only 1D displacement time series along the satellite line of sight (LOS), as provided by InSAR? (ii) How do InSAR-derived displacement trend estimates differ from those obtained through traditional topographic monitoring? (iii) Can a data fusion approach, integrating both InSAR and topographic data, provide more accurate results than using either method alone? Topographic monitoring, which offers direct three-dimensional measurements, is used as the “ground truth” for evaluating the accuracy of InSAR and data fusion methods. The results show that, even though only SAR images from a single orbital geometry are available, InSAR can provide reasonably accurate estimates along the slope-aligned direction, while it is less effective in capturing transverse displacements due to the limitations of measuring along the satellite’s LOS. However, when combined with prior knowledge of landslide behavior, InSAR still provides valuable insights. Bayesian data fusion, which integrates topographic and InSAR measurements, significantly reduces uncertainties, particularly in short monitoring periods, offering a cost-effective alternative to continuous topographic monitoring. Additionally, this study explores two alternative strategies: limiting topographic measurements to the first year and spreading sparse topographic measurements over several years and relying on satellite data thereafter. While both approaches yield satisfactory results in the slope direction, they show higher uncertainties in the transvers direction, particularly as the frequency of topographic measurements decreases. The findings suggest that a combined monitoring approach, integrating satellite and topographic data, as well as a prior knowledge of landslide behavior, provides an accurate and cost-effective solution for long-term monitoring of infrastructure in landslide-prone areas.

A novel data-driven damage detection framework, based on probabilistic modeling of mel-frequency cepstral coefficient (MFCC) distributions, is proposed. This study replaces traditional autospectral densities with the power spectrum derived from cross-spectral density ratios in MFCC extraction, enhancing sensitivity to subtle dynamic changes across structural locations. The Bhattacharyya distance (D_B) is introduced to quantify dissimilarities between MFCC distributions under baseline and potential damage scenarios. Subsequently, a damage indicator (DI_B) based on the Bhattacharyya distance is proposed. To mitigate uncertainties caused by environmental noise and measurement variability, a statistically sound threshold is established through Bayesian resampling and Monte Carlo simulation. When the DI_B values of structural states exceed this threshold, it indicates the presence of damage. Additionally, a vectorization scheme is employed to improve computational efficiency, enabling faster processing of multichannel data. The accuracy and effectiveness of the proposed method are validated through a laboratory experiment involving four beams and a field test conducted on a steel truss bridge. The results demonstrate the proposed method’s ability to detect and classify damage states accurately under diverse conditions, highlighting its applicability for reliable structural health monitoring (SHM).

Developing fully automated operational modal analysis (AOMA) algorithms is a critical yet challenging task in structural health monitoring, with urgent practical engineering demands. This study introduces a robust AOMA framework that leverages an enhanced stabilization diagram and hierarchical density estimation strategy to address these challenges. The main innovations of this framework are threefold: (1) A comprehensive physical mode validation strategy that effectively eliminates more stubborn spurious poles by destroying the noise structure inherent in the identified system matrix. (2) A hierarchical density clustering approach for the automatic interpretation of stabilization diagrams, which eliminates the need for manual threshold selection and adapts seamlessly to varying-density clustering scenarios. (3) A novel representative mode selection approach based on clustering exemplars is presented, resulting in a stronger consistency of the selected modal parameters. Hierarchical clustering of modal poles, optimal cutting of clustering trees, outlier rejection, and cluster quality validation are integrated in a single framework, streamlining the analysis and avoiding tedious postprocessing steps. The robustness and applicability of the algorithm are extensively validated using a numerical building structure, the Z24 bridge benchmark test, and a footbridge equipped with a long-term continuous monitoring system. The results demonstrate that the proposed framework achieves robust AOMA on long-term field measurement data without any user intervention. The applicability of the algorithm to closely spaced modes and long-term modal tracking tasks is also demonstrated. This study advances the field of AOMA by offering a scalable, efficient, and accurate solution for real-time structural health assessment, with potential extensions to broader engineering applications.

In this article, two advanced imaging-based metrology methods, the multipoint method and the tele-wide-angle method, are introduced to the field of structural health monitoring. Both provide the means to significantly improve either the measurement uncertainty or the field of view compared to classical imaging-based methods. The multipoint method utilizes a computer-generated hologram to replicate a single object point to a predefined spot pattern in the image. Spatial averaging of the spot positions improves the measurement uncertainty. The second method, called tele-wide-angle, uses a diffraction grating to considerably enlarge the field of view of a tele objective lens. Both methods are investigated regarding the achievable measurement uncertainty at distances between 34 and 50 m. The standard deviations of the error range between 0.027 and 0.034 mm for the multipoint method and 0.008 and 0.02 mm for the tele-wide-angle method. In the second part of the article, both measurement systems are employed in a field study, measuring the deformation of a railway bar arch bridge. An inductive displacement transducer and several accelerometers are installed to validate the measured displacements and dynamics.