Structural Control & Health Monitoring最新文献

Bearings are critical components of bridges and are susceptible to various forms of deterioration under the action of traffic loads and complex environmental conditions. Existing methods for assessing the condition of bearings, including visual inspections, force sensors, cameras, and vibration sensors, still present challenges in accurately locating and quantifying disengagement. This paper proposes a novel data-driven damage index based on the bearing-to-beam displacement relation under round-trip trains for disengagement monitoring of high-speed railway (HSR) bridge bearings and provides a rapid and efficient evaluation scheme using a noncontact visual measurement system. The dynamic responses of a spatial elastically supported beam subjected to moving loads are first derived, and a mathematical expression has been theoretically established to describe the relation between the damage index and the bearing stiffness. A numerical three-dimensional (3D) train–bridge interaction (TBI) model is developed to validate the efficacy of the suggested approach. Finally, the feasibility of integrating noncontact visual measurement schemes in the disengagement monitoring of HSR bridge bearings has been successfully validated by conducting an on-site experiment on the Yangcun Bridge. The research findings indicate that the proposed damage index exhibits remarkable insensitivity to noise under the random traffic flow, showing good damage localization and anti-interference capabilities. The established mathematical expression accurately reflects the relation between the damage index and the bearing stiffness, and it can be considered in an actual test that bearing disengagement has occurred when the proposed damage index is larger than 0.5. The proposed methodology offers a rapid, accurate, and noncontact approach for the disengagement monitoring of HSR bridge bearings, contributing to the long-term operational safety of bridges.

Bolted joint structures are critical fastening components across various engineering applications, and the ability to monitor their contact status is crucial for effective structural health monitoring (SHM). The acoustic emission (AE) technique combined with deep learning (DL) methods has been extensively applied in bolt looseness monitoring. Current DL methods assume that the data distribution remains consistent between training and testing datasets. In fact, the surface contact state and the resulting AE signal will be different after each assembly. To address the domain shifts caused by variations in surface contact states and AE signal characteristics across different assemblies, this paper presents a domain-generalized framework using acoustic emission (DGFAE) for bolt looseness diagnosis without requiring prior access to target domain data. The framework integrates a compound loss function capturing the ordinal progression of bolt loosening and employs deep correlation alignment (Deep CORAL) to enhance feature alignment across domains. The effectiveness of the DGFAE method is validated using the “ORION-AE” dataset, with ablation experiments and comparative analysis against other domain generalization (DG) techniques. Compared to state-of-the-art DG methods, superior diagnostic accuracy is achieved under unseen target conditions. Furthermore, a pseudo- DG scenario is explored, where partial healthy samples from the target domain are assumed to be accessible, and the Mixup augmentation technique is integrated to further improve generalization robustness. The diagnostic results confirm that the proposed DGFAE method provides a practical and effective solution for bolt looseness monitoring in real-world engineering settings.

Structural control plays a critical role in protecting civil structures from earthquakes and other external disturbances. Among various strategies, active control has been widely studied, which uses actuators to apply counteracting forces based on control algorithms. Instead of traditional control theories, recent advances in machine learning have motivated the exploration of deep reinforcement learning (DRL) as a new paradigm for active structural control. This study investigates the feasibility of DRL-based seismic response mitigation, focusing on whether DRL can realize control force characteristics and response reductions consistent with the design intent of structural control engineers. In this research, the proximal policy optimization (PPO) algorithm is adopted as a representative DRL method suitable for continuous control tasks. The training environment incorporates domain randomization in ground motion generation using a Kanai–Tajimi filter, enabling the agent to adapt to diverse seismic excitations. To verify the effectiveness of the proposed approach, three numerical examples are examined, including single- and multistory structural models with one or two active bracing systems. Numerical simulation results demonstrate that the trained controllers achieved significant reductions in story displacements, interstory drifts, and accelerations, while generating force–displacement hysteresis loops that reflected the intended reward design. Depending on the reward formulation, the controllers also exhibited restoring-force characteristics resembling negative stiffness, demonstrating the flexibility of DRL-based approaches in capturing diverse structural behaviors. Furthermore, the controllers maintained robust performance against a wide range of previously unseen disturbances. These findings highlight DRL and PPO, in particular, as a promising framework for next-generation active structural control under seismic loading.

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.