Structural Control & Health Monitoring最新文献

Hydraulic shock absorbers in passenger vehicles typically generate damping through valves and orifices that create a restricted fluid passage between the cylinder’s upper and lower chambers. Motivated by the proven effectiveness of inerters in various applications, this study investigates the integration of hydraulic inertance into this fluid passage to enhance absorber performance. While prior research has explored such integration, a systematic method for identifying optimal configurations of hydraulic stiffness, damping and inertance elements within the passage remains undeveloped. To address this gap, this study proposes a novel configuration-optimisation framework for hydraulic absorbers using a predefined number of each element type. The absorber is modelled as a three-terminal hydraulic network, and a graph-based enumeration method is introduced to generate all feasible network layouts. Each candidate is then tuned and evaluated to determine the optimal design, which is subsequently realised using physical components considering necessary nonlinear and parasitic effects. A numerical case study involving a simplified car model demonstrates the framework’s effectiveness. The trade-off between ride comfort and road handling ability is investigated. For a comfort-oriented design scenario, using just one stiffness, one damping and one inertance element, the proposed method identifies a physical design that improves ride comfort by 19.4% compared with a conventional absorber with a single orifice in the fluid passage.

The unclear impact of temperature on rockfill dam settlement and the lack of a solid basis for selecting temperature parameters in prediction models are problematic. These issues significantly limit the accuracy and applicability of deformation monitoring models for rockfill dams. For this reason, a method for decomposing the settlement components of rockfill dams, along with an intelligent prediction approach, is proposed. The Bayesian optimization (BO) algorithm is employed to optimize the hyperparameters of the Bayesian dynamic linear model (BDLM), enabling a comprehensive exploration of the correlation between rockfill dam settlement and temperature factors. Based on this, a BO–BDLM-based decomposition model is constructed to quantify the contribution of the temperature factor to settlement behavior. Spatiotemporal analysis is conducted to uncover the evolution patterns of various influencing components, revealing the underlying mechanism by which temperature affects settlement. Furthermore, both a full-feature model and a simplified prediction model are developed to predict settlement, and their prediction accuracies are compared. The contribution of the temperature factor is quantitatively assessed using the SHapley Additive exPlanations (SHAP) method. Example analyses demonstrate that our BO–BDLM significantly improves performance and accurately isolates the temperature factor consistent with rockfill dam deformation characteristics. The temperature component contributes approximately 2%–4% of total settlement but accounts for 38.39% of model importance. This pivotal factor substantially enhances prediction accuracy. By quantitatively assessing temperature influence and establishing its selection basis, our study offers valuable insights for the safety evaluation of rockfill dams and related engineering projects.

Tuned mass dampers (TMDs) are crucial for mitigating excessive structural vibrations. Accurate acquisition of TMD parameters and responses from limited data is vital for assessing TMD performance and structural safety. Conventional physics-based methods require ideal environmental conditions, while pure data-driven approaches face limitations in generalization and interpretability. To address these issues, this study proposes a physics-informed neural network (PINN) that synergizes physical principles with data-driven techniques for TMD parameter identification and response prediction. The governing equations of TMD motion are embedded into a multilayer perceptron (MLP) architecture as physical constraints. Task-specific loss functions are designed for distinct tasks, and a tailored adaptive moment estimation (Adam) optimizer is utilized. To examine the performance of the proposed PINN-based method, it is applied to a single-degree-of-freedom (SDOF) system with a TMD. The results show that the proposed method can accurately identify the TMD parameters and predict the TMD responses. A comprehensive analysis is further conducted to evaluate the influence of key factors including observation noise, the number of training data points, sampling frequency, model hyperparameters, and physical equation errors. Additionally, the PINN-based method is compared with the data-driven method to validate the effectiveness of the proposed method.

Offline iterative control (OIC) is a widely employed technique in shaking table tests for accurately reproducing earthquake waveforms. However, repeated offline iterations can cause cumulative damage to fragile specimens, while the continuously changing dynamic characteristics of nonlinear specimens reduce the control accuracy of OIC. To overcome these limitations, real-time iterative control (RIC) has been introduced and applied to eliminate the need for multiple iterations. To further improve the stability and accuracy of RIC, this study introduced RIC with online system matrix correction (RICSC) method, discussed the control performance of the RICSC method. The RICSC method evaluates the accuracy of the identified system matrix using the following indices: the coherence function (CF) weighted sum, the CF, and the autocorrelation power density spectrum (AS). Based on these evaluations, the system matrix is corrected via frame correction (FC) or frequency point (FP) correction algorithms, thereby enhancing waveform reproduction accuracy and control stability. The performance of the RICSC method was verified via numerical simulations and shaking table tests under 20 test conditions. The results show that the FP correction algorithm (RICSC-FP) achieves the fastest convergence of absolute error, and its reproduction accuracy is higher than those of the traditional RIC and FC (RICSC-FC) algorithms. Both numerical and experimental results demonstrate that the RICSC method provides higher reproduction accuracy than OIC after just one iteration.

This study proposes an integrated framework for optimal sensor layout, data expansion, and damage detection in truss structures. Mode shapes used for sensor optimization are reconstructed from sparse measurements via pseudoinverse-based modal expansion. Based on these expanded mode shapes, optimal sensor layouts are determined using effective independence (EI), QR decomposition, and a genetic algorithm guided by the Modal Assurance Criterion (GA-MAC). Damage localization is achieved through the computation of modal strain energy (MSE) and its relative deviation (dMSE) at the element level. A planar 19-node truss model serves as the numerical benchmark for evaluating the proposed methodology. Monte Carlo simulations with sensor noise are conducted to establish statistical thresholds for robust damage identification. The results demonstrate that the GA-MAC approach outperforms conventional methods in both response reconstruction accuracy and damage detection reliability, achieving high true positive rates while maintaining low false positive rates (FPRs). This study contributes to advancing practical strategies for structural health monitoring (SHM) of truss systems by enhancing detection accuracy, noise robustness, and scalability. The study’s integrated pipeline includes the following: GA-MAC–based sensor layout, modal expansion for response reconstruction, and dMSE-based damage detection with Monte Carlo thresholding. In particular, under the multiple-damage scenario, the GA-MAC configuration achieved a true-positive rate (TPR) of 100% and a FPR of 0%, which represents approximately an 8% improvement in TPR and a 5% reduction in FPR compared to the EI method.

The deformation of concrete dams directly reflects their structural health and operational state, serving as a critical foundation for safety assessment and early risk warning. Accurately predicting dam deformation patterns is thus essential for ensuring long-term structural safety and enabling scientific operation management. However, existing models remain limited in addressing high-frequency noise in monitoring data, performing dynamic feature selection, and modeling complex spatiotemporal dependencies, which collectively constrain prediction accuracy. To overcome these challenges, this study proposes a dam deformation prediction model that integrates variational mode decomposition with wavelet thresholding (VMD–WT), a partial autocorrelation function (PACF)–based dynamic feature selection approach, and the ScaleGraph Block-Mamba-like linear attention (SGB–MLLA) –Transformer. The proposed model performs multiscale signal decomposition to suppress noise and extract dominant deformation trends, while dynamically selecting key influencing factors and incorporating spatial dependency modeling and lightweight attention mechanisms to enhance the representation of long sequence and multifactor coupled deformation features. To validate the model’s effectiveness, deformation data from monitoring points of a concrete dam in Jiangxi Province, China, were used for evaluation. Experimental results demonstrate that the proposed model achieves superior prediction performance across multiple monitoring points, achieving near-perfect accuracy (R² = 0.9993) with submillimeter error margins at GLD4, significantly outperforming existing models. These findings confirm that integrating frequency-domain decomposition with adaptive feature selection and employing linear attention for efficient long sequence modeling can substantially improve deformation prediction accuracy. This research provides a novel methodological framework for dam health diagnosis and safety management, offering both theoretical and practical value for the development of intelligent dam monitoring systems.

In this paper, the continuous deformation monitoring data of high arch dams during construction are obtained using the shape accel array (SAA) for the first time. First, the accuracy of the SAA measurement was tested in the laboratory. Then, the SAA was installed using the new method on a case dam section to obtain continuous deformation data during the construction period of the high arch dam. Finally, the self-developed finite element simulation software SAPTIS was used to conduct a simulation analysis of the case dam, considering the effects of concrete material creep, self-volume changes, water cooling, environmental temperature, and self-weight. The laboratory test results show that deformation measurement accuracy is significantly improved after noise reduction by wavelet analysis. The continuous deformation of the dam during construction can be monitored in real time by embedding SAA in the construction of the case dam section. Then, the finite element simulation results verify the accuracy of the measured results of the dam and quantify the impact of various factors on dam deformation. SAA provides an effective means for real-time monitoring and safety assessment of dam deformation.

In this study, an automated transmissibility-based procedure for damage detection is developed for output-only systems. The application of transmissibility has been previously investigated for damage detection. Despite the advancements, current techniques are not applicable in a general way, as vast experience or expert knowledge is needed to achieve accurate results, particularly for the frequency range selection. Moreover, the extent of noise influence still needs to be adequately addressed. A novel procedure has been developed to resolve these issues. First, the frequency range is determined by applying modal coherence using the first singular value of the cross-power spectral density (CPSD). Then, the transmissibility functions are calculated from the CPSD and smoothed using a moving mean approach to reduce the influence of noise. Afterward, the threshold is obtained from the transmissibility damage indicator values of the system’s healthy state. Finally, damage detection can be performed continuously for each subsequent dataset. The procedure is compared to damage detection based on eigenfrequencies and mode shapes using simulated data, demonstrating higher sensitivity to minor damages at low noise levels. Furthermore, the procedure is validated on experimental data from a steel cantilever beam, where various noise scenarios, damage severities, and damage positions are considered, and on field data from a lattice tower, showing high damage detection accuracy across three damage scenarios. The proposed procedure can be automated, demonstrating sensitivity to minor damages when high signal-to-noise ratio is available.

Pavement intersecting cracks expand outward under load, especially at intersections where stress leads to branching and a complex network. This study introduces Crack-DL, a deep learning framework for crack segmentation and feature extraction. We propose the YOLO-Segcrack model, which integrates the advanced FasterNet backbone with the SENet attention module. This combination leverages the computational efficiency of FasterNet for robust feature extraction and the discriminative ability of SENet to emphasize critical crack areas, and the model achieves significantly improved segmentation performance and precisely extracts pavement intersecting cracks. Additionally, a convolution kernel matching algorithm (CKMA) is developed based on morphological image processing for precise intersection point localization and to quantify crack lengths and intersection angles. Finally, the CrackX dataset containing pavement intersecting cracks is constructed to support this research. The proposed Crack-DL framework was tested on CrackX and public datasets, CrackTree260, demonstrating its accuracy and reliability. Experimental results show that using the YOLO-Segcrack model increases detection and segmentation precision by 11.1% and 4.8%, respectively. In addition, extensive experimental results on crack-seg, package-seg, and carparts-seg datasets further show that the improved YOLOv8s-seg model outperforms existing advanced methods in terms of performance. When applying the CKMA for detecting intersection points, the detection accuracy reached 73.19%. For the publicly available CrackTree260 dataset, the accuracy reached 91.5%. Furthermore, when the error is under 5 unit pixels (mm), the accuracy for calculating total crack length is 92.46% for ground truth images and 80.82% for the adaptively segmented binary images. These results demonstrate that the proposed model enhances the extraction of intersecting cracks area and the CKMA provides a reference value for the analysis of cracks propagation. The dataset and source code are available at https://github.com/Keeeram/Intersecting-Crack-Analysis.

Wind turbines with larger capacities face bending deformation due to taller towers and longer blades, necessitating mitigation against extreme seismic loads. A vertically installed inerter-based damper, referred to as the tuned viscous mass damper (TVMD), is proposed alongside a closed-form design approach. First, the mechanical model and simulation approach for the TVMD and wind turbines are introduced, followed by the derivation of governing equations and frequency response solutions, considering the parked state. Second, a nacelle-hub assembly displacement–oriented design principle is formulated, providing mathematical design expressions and closed-form solutions based on the generalized fixed-point principle. Finally, the effectiveness of the proposed framework is validated through design cases and comparative investigation of theoretical approaches, under parked conditions with negligible aerodynamics and thus low effective damping, highlighting the advantages of the closed-form design formulas. The results indicate that the vertically installed TVMD offers superior performance compared to traditional damping design approaches in wind turbines, enabling the simultaneous control of multiple seismic responses. Furthermore, the nacelle-hub assembly displacement–oriented design principle and closed-form design formulas provide a quantitative framework for optimizing key design parameters of vertical TVMDs, facilitating rapid design implementation and deeper theoretical understanding. In addition, the proposed closed-form design formulas ensure enhanced energy dissipation and specific modal tuning capacity, offering robustness against parameter variations.