Structural Control & Health Monitoring最新文献

Machine learning algorithms have significantly advanced structural monitoring by achieving accuracy levels outperforming traditional methods. These approaches facilitate uncertainty modeling and statistical pattern recognition analysis, supporting decision-making and manipulating broader data fusion. Efficient condition assessment of bolted structures, widely used in engineering systems and structural steel members, is crucial for maintaining stability, preventing unwanted loosening, and enabling scheduled maintenance. A critical issue in bolted systems, torque loosening, is often caused or aggravated by excessive vibrations, shocks, temperature variations, and improper usage, increasing the risk of structural faults. Predicting and monitoring bolt loosening remain a significant challenge, as it typically requires expensive inspections and operational controls. This work proposes an enhanced machine learning–based condition assessment model for estimating bolt torque loosening using the spectrum of raw vibration signals and data-driven augmentation strategies. The condition monitoring accounts for intrinsic variability introduced during the assembly process, with damage indexes derived from dynamic responses serving as feature extractors. The machine learning model utilizes data augmentation and fusion to enhance the dataset, relying solely on experimental data, thereby eliminating the need for numerical models. The results demonstrate significant enhancement in the model performance by adopting the integrated dataset, yielding improved torque estimation accuracy with lower error rates. In addition, the monitoring process incorporates uncertainty quantification associated with torque estimation, providing a more reliable assessment of the system’s condition. Furthermore, this study highlights the potential of data-driven machine learning damage assessment techniques in bolted joint monitoring, providing an effective and efficient method for detecting bolt torque loosening using raw vibration spectra. The proposed approach accelerates inspection and establishes a robust technique for monitoring bolted systems.

Eddy current damper (ECD) has emerged as a highly desirable solution for vibration control due to its exceptional damping performance and durability. However, the inherent nonlinearity of the ECD poses significant challenges in research and engineering implementations. Traditional views attribute the nonlinearity of the ECD solely to variation in velocity. However, experimental results reveal that nonlinearity still exists even at a constant velocity. The nonlinearity at a constant velocity has not been sufficiently emphasized and quantitatively modeled. This study addresses the issue by developing a dynamic theoretical model with clear physical meaning and a simple mathematical form. A comprehensive study of the nonlinear characteristics of the ECD has been carried out using a combination of experimental and theoretical analysis. Firstly, the basic construction and working mechanism of a velocity-amplified hamburger-shaped eddy current damper (VHECD) are described in detail. Subsequently, a prototype experiment is conducted to explore the mechanical performance of the VHECD. Most importantly, a nonlinear phenomenon at a constant velocity is revealed and a dynamic theoretical model is developed. Finally, the dynamic theoretical model is validated through the experimental results of the VHECD and numerical simulation of a single-degree-of-freedom (SDOF) system. The proposed dynamical theoretical model generalizes the nonlinear phenomenon at a constant velocity. Both the coefficient of determination of force and the mean absolute percentage error of energy dissipation show that the dynamic theoretical model performs exceptionally well. The numerical simulation of the SDOF system demonstrates that the proposed dynamic theoretical model can more accurately predict the damping performance of ECD than the Wouterse model. This dynamic theoretical model is useful for the physical understanding of the ECD and the engineering application.

Displacements are among the most important engineering response parameters to be monitored during shake-table testing, with experiments playing a key role in studying the seismic behavior of structures. However, their accurate measurement is not a trivial task when using contact sensors. Computer vision is an attractive alternative for monitoring absolute and relative displacements, and this study presents a new configuration to fully exploit its potential. The proposed solution combines internal and external video cameras. The former is installed on the roof and points downwards to simultaneously acquire the displacements of targets located throughout the height of the building. The latter was installed outside the shake-table platen and tracked the roof displacements to provide redundant measures for control and noise compensation. In this way, the movements of the buildings can be reconstructed with high robustness and precision using a limited number of video cameras. The proposed configuration was applied for the first time during shake-table testing of a full-scale six-story building on the outdoor shake table at the University of California, San Diego. The measurements obtained up to strong dynamic inputs showed the capacity of the proposed approach in real-world environmental conditions and were used for a critical comparison with conventional contact sensors.

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.