Structural Control & Health Monitoring最新文献

A hybrid structural health monitoring (SHM) system is developed by integrating the interstory drift angle method and the Hilbert–Huang transform (HHT) analysis into a comprehensive framework. This approach seeks to provide a comprehensive damage detection capability, seamlessly bridging the assessment of linear behavior under minor excitations with the sensitive detection of nonlinearity and stiffness degradation under severe loads. The proposed SHM system comprises two individual methods: the interstory drift angle method, which mainly focuses on the linear behavior of the structure, and the HHT-based analysis, which is employed to detect structural nonlinearity. The first part focuses on detecting the displacement of interstory drift in each floor under minor excitation. Data measured by accelerometers installed on the structure are converted into floor displacements, and the drift angles between different floors are calculated, reflecting the health conditions of each floor. The second part utilizes the superior capability of the time–frequency domain of the HHT to analyze the vibration signals measured under external forces. The relationship between structural behavior and nonlinearity is explored by identifying the dynamic parameters of the structure within the time–frequency domain magnification function, thereby defining a damage index (DI). A shaking table test was conducted on a six-story steel frame model to verify the feasibility of this system. The system achieved more than 97% similarity with measured displacement at low intensities, captured dominant frequency softening from 1.12 to 0.46 Hz, and produced DI values increasing from 0.34 (healthy) to 0.79 (severely damaged). The results show that interstory drift angles and the HHT-based nonlinearity can serve as effective cores for SHM, providing an important basis for the safety assessment and maintenance of building structures. By accurately identifying the possible damage of the structures, the developed SHM system can enhance disaster resilience under extreme conditions such as earthquakes.

The evolution pattern of dam deformation reflects its structural response and operational state. Analyzing this pattern enables effective identification of the probability of deformation anomalies. Deviation reflects the extent to which dam deformation deviates from its expected evolution pattern and serves as an important basis for identifying deformation anomaly behavior. However, traditional deformation anomaly assessment methods overlook the distribution of extreme values within the deviations and the complex dependencies between measurement points, limiting the reliability of deformation anomaly assessment results. To address these limitations, this study proposes a regional deformation anomaly assessment method considering extreme-value distribution of deviations. Initially, the improved temporal fusion transformer (ITFT) prediction model is employed to capture the temporal evolution pattern of dam deformation and compute the deformation deviations at measurement points. Subsequently, extreme-value theory (EVT) is applied to establish a generalized extreme-value distribution for the deviation extremes, and these distributions are used to correct the probability density function of deviations estimated by kernel density estimation (KDE), and this process determines the deformation anomaly rates for single measurement points. Finally, measurement points with similar deformation patterns are clustered using Ward’s hierarchical clustering algorithm, while the Frank copula model captures intraregion nonlinear dependencies for regional deformation anomaly assessments. The engineering application verifies that the proposed method accurately captures the extreme-value distribution of deformation deviations and the complex dependencies between measurement points. This enhances the reliability and effectiveness of arch dam deformation anomaly assessment, providing a scientific basis for arch dam safety monitoring.

Steel-truss rigid-tied arch bridges are among the most important structural forms of high-speed railway bridges in China. Train-flow monitoring data indicate that the train loads associated with multiline intersections account for 46.22% of the total train load. The fatigue performance of rigid shortest hangers under train loads at multiline intersections is important. Based on the engineering background of the Nanjing Dashengguan Yangtze River Bridge, which is the first six-line railway bridge in the world, the fatigue performance of the shortest hangers under train loads at multiline intersections is first evaluated via long-term dynamic strain monitoring. Furthermore, the effects of train loading parameters such as the number of train intersections and the driving direction on the axial-bending effect and fatigue performance of the shortest hanger are analyzed. Then, the fatigue performance parameters of all the shortest hangers of the bridge in 5 cases involving multiline intersections are analyzed through numerical finite-element simulations, and the annual cumulative fatigue damage of all 12 shortest hangers considering the axial-bending effect is calculated according to the monitored train loads. Finally, the inspection periods of the shortest hangers are recommended on the basis of the degree of fatigue damage. The fatigue performance of the shortest hangers is significantly affected by multiline intersections. Moreover, the bending strain of the shortest hangers has a significant effect on the fatigue effect and is positively correlated with the number of train intersections. The maximum value of annual fatigue damage is calculated for the shortest hanger at the southern end of the first span of the middle truss. The results provide a basis for decision-making involving the detection, maintenance, and management of the shortest hangers of steel-truss rigid-tied arch bridges.

Evaluating the stability of seawalls constructed on soft soils is critical but challenging. Traditional methods often depend on whether settlement velocity exceeds predefined thresholds, which can overlook subtle settlement fluctuations and may be less adaptable to varying construction and environmental conditions. To overcome these limitations, this paper presents a novel evaluation framework that combines a new settlement-to-loading index with a permutation entropy (PE) algorithm. By incorporating both settlement velocity and loading, the proposed index captures the behavior of seawalls under complex load conditions more comprehensively than fixed settlement velocity thresholds. The PE algorithm is then employed to analyze the time-series data of the settlement-to-loading index, enabling the detection of small-scale, transient fluctuations, which is a critical feature for soft soil scenarios characterized by significant and sporadic settlement spikes. A case study of a seawall in China demonstrates that this combined approach is more sensitive than conventional methods, effectively signaling early instabilities resulting from minor construction activities or rapid loading changes. Overall, the proposed method offers a physically meaningful, adaptable, and practical approach for evaluating seawall stability on soft soils, potentially reducing misjudgment in coastal infrastructure projects.

Seismic isolation systems with adaptive behavior are critical for ensuring structural resilience across varying earthquake intensities. Variable curvature friction pendulum bearings (VC-FPBs) offer a promising solution by providing displacement-dependent stiffness and enhanced energy dissipation. This study investigates the size effect on the seismic performance of VC-FPBs through experimental testing and finite element simulations. Five VC-FPB specimens of different scales were subjected to cyclic quasistatic tests to evaluate their force–displacement responses, adaptive stiffness characteristics, and frictional behavior. Results revealed that smaller specimens failed to replicate the full-scale adaptive stiffness behavior due to geometric limitations, stress distribution differences, and friction pad wear mechanisms. Modified small-scale specimens with enhanced curvature profiles restored the intended stiffness softening behavior. Numerical models successfully captured the experimental trends, validating the influence of geometric scaling on mechanical performance. These findings highlight the necessity of thoughtful modifications in scaled VC-FPB models to ensure accurate representation of full-scale behaviors for seismic isolation applications.

The advent of Synthetic Aperture Radar (SAR) imaging has presented the possibility of remote monitoring of civil infrastructure on a large scale. Although well established for observing slow and long-term phenomena, its application to vibration-based structural health monitoring (SHM) remains relatively unexplored in the current literature. This study demonstrates the use of micro-Doppler SAR (MDSAR) using data from spaceborne platforms for measuring structural vibrations of a real bridge, specifically the line of sight velocity time histories of the deck. These measurements are compared to synchronous ground truth data to validate the method and assess its accuracy. Experimental results show that MDSAR measures vibration with an error in velocity on the order of 1 mm/s and successfully identifies the bridge’s dominant frequencies from two separate SAR acquisitions at different times. Spectral correlation with ground truth data reaches values up to 0.88. Frequency estimation errors are essentially controlled by the resolution of the spectrum, which in turn is limited by the acquisition time. In this work, a frequency resolution of 0.06 Hz is achieved for an acquisition duration of 16 s. Given these results, it is expected that MDSAR could be suitable for monitoring natural frequencies and performing modal recognition for bridges. Further improvements in the technology and in the analysis algorithm could potentially enable the accurate measurement of mode shape components.

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.