Structural Control & Health Monitoring最新文献

Traditional statistical models for arch dam deformation monitoring consider only functional equivalence between independent variables and deformation components (hydrostatic pressure, temperature, and time effects). However, they neglect the structural characteristic of compatible deformation between horizontal arch rings and cantilever beams under complex loading conditions. This study analyzes deformation mechanisms of arch dams under reservoir hydrostatic pressure and thermal loads based on arch-beam load distribution principles. By examining load sharing and corresponding deformations of arch rings and cantilever beams within the dam system, we derive radial deformation expressions from the cantilever perspective, accounting for beam and foundation deformations. This establishes a mathematical formulation for radial deformation under hydrostatic pressure. Temperature loading is decomposed through the dam thickness into uniform temperature, equivalent linear temperature gradient, and nonlinear temperature gradient. Neglecting the nonlinear gradient (which primarily affects local surface deformation and stress), we develop separate radial deformation expressions for uniform temperature and linear temperature gradient. This yields a functional relationship for the temperature component of arch dam deformation. Building on these foundations, we construct a novel statistical model for analyzing concrete arch dam deformation behavior. Its validity and scientific rigor are demonstrated through engineering case studies.

G. Wei, Z. Mu, Y. Li, Y. Qi, and G. Feng, “Dynamic Horizontal Displacement Evaluation Method of Shield Tunnel Based on MSD Method for Basement Side Tunnels,” Structural Control and Health Monitoring, vol. 2025 (2025), https://doi.org/10.1155/stc/5170617.

In the original version of the article titled “Dynamic Horizontal Displacement Evaluation Method of Shield Tunnel Based on MSD Method for Basement Side Tunnels,” there was an error in the title where the word “Tunnel” was accidentally repeated.

This has been corrected in the article, and we apologize for this error.

To address challenges posed by insufficient feature extraction, difficulties in capturing complex patterns, limited prediction accuracy, and unquantified uncertainty inherent in traditional point prediction models for complex time series data, this study proposes a novel interval prediction framework based on temporal convolutional network (TCN)–bidirectional gated recurrent unit (BiGRU)–self-attention mechanism (SATT)–adaptive bandwidth kernel density estimation (ABKDE), specifically tailored for earth–rock dam seepage flow prediction. Initially, the TCN is employed to extract essential temporal features from seepage monitoring data. Subsequently, these extracted features are input into a BiGRU, effectively capturing both historical dependencies and future-oriented information. Following this, a SATT dynamically assigns weights to critical features, thereby enhancing the predictive relevance and forming a high-accuracy point prediction model. Finally, utilizing point prediction error distributions combined with ABKDE and bootstrap methodology, statistically robust intervals at multiple confidence levels are constructed. This integrated approach comprehensively addresses feature extraction, complex time series modeling, and uncertainty quantification. The case conducted demonstrates that the proposed TCN–BiGRU–SATT model consistently outperforms both benchmark models and the simpler BiGRU–SATT in evaluation metrics, indicating superior accuracy and stability. Leveraging residuals derived from point predictions, the ABKDE component adaptively adjusts bandwidths, effectively capturing and quantifying the uncertainty inherent in predictions. Performance metrics at distinct confidence intervals surpass those obtained using conventional kernel density estimation (KDE), confirming greater adaptability and responsiveness to variations in data. Specifically, at confidence levels of 85%, 90%, and 95%, the integrated evaluation index F attains values of 1.6447, 1.5821, and 1.3885, respectively, corresponding to improvements of 9.02%, 9.59%, and 4.05% over the KDE method. These findings underscore the practical value and potential applicability of the proposed methodology in engineering contexts.

A novel self-centering (SC) rotational wedge–shaped friction damper (SRWFD), which can be used as the high-performance connector for the joints of beam–column, column–foundation, and coupling beam–shear wall, was proposed and investigated in this study. The friction–self-regulating function ensures a positive correlation between the axial compressive stress of the annular friction plates and the overall rotational angle of the damper. This design not only maintains excellent energy-dissipation (ED) capability but also obviously reduces the additional restoring moment required during the unloading process, which is conducive to improving the SC capacity of the damper and reducing the cost. The structure and working principle of the SRWFD were first illustrated, followed by systematically experimental and numerical investigations to verify the innovative functional design and to reveal the influence of the material and pretightening force (PF) of superelastic SMA bolts, the friction coefficient between friction plates and connecting plates, the friction coefficient between wedge-shaped protrusions and grooves, the effective diameter of SMA bolts, and the height of wedge-shaped protrusions on the cyclic rotational behavior of the SRWFD. Results showed that the damper exhibited a symmetric flag-shaped hysteresis characterized by satisfactory SC capacity and ED capability. Furthermore, when the protrusion slope is relatively small, the SC capacity of the SRWFD exhibits a small variation with increasing slope. However, once the protrusion slope exceeds a critical threshold, the overall rotational angle recovery ratio of the SRWFD rapidly increases from approximately 7.3% to about 97.2%. This phenomenon validates the innovative functional design of the novel SRWFD, which leverages the combined structure of annular hard friction plates, wedge-shaped protrusions, and superelastic SMA bolts to achieve the friction–self-regulating function. As a result, the restoring moment required to unload the device to a zero rotational angle is significantly reduced, and only a small critical restoring moment is needed to ensure excellent SC performance.

R. Chen, Y-Q. Ni, “A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data,” Structural Control and Health Monitoring, 2023, https://doi.org/10.1155/2023/9271433.

In the article, there are errors in Equation 11 and Figure 13, introduced during the production process.

In Figure 13, “(Ni and Chen, 2021)” should read “[46].” The correct Figure 13 is shown below:

Lastly, in Section 2.4, the following sentence is incorrect:

“Hence, sampling z_i with equation (11) admits a new component, apart from other existing components, to be created and either to grow up or fade away in a probabilistic manner during the Gibbs iterations.”

Should read:

“Hence, sampling z_i with equation (13) admits a new component, apart from other existing components, to be created and either to grow up or fade away in a probabilistic manner during the Gibbs iterations.”

We apologize for these errors.

Wind energy plays a pivotal role in the transition to sustainable power generation. However, maintaining the reliability and efficiency of wind turbine (WT) remains a significant challenge due to complex operational conditions and the high cost associated with unexpected failures. Effective condition monitoring (CM) and predictive maintenance (PM) strategies are critical to mitigate these risks. This study presents a data-driven fault detection framework that fuses supervisory control and data acquisition (SCADA) data with high-frequency vibration signals using deep learning techniques to enhance diagnostic performance. Unlike conventional normal behavior models that rely exclusively on healthy data for training, the proposed framework incorporates limited labeled fault data when available. As only a few types of faults and a few samples are typically available in real-world scenarios, the approach does not assume a complete representation of all possible fault conditions. Instead, it is designed to generalize beyond the specific faults seen during training. This is demonstrated by training the model on healthy conditions and only two known fault types (with labeled data available) and testing it on a third, previously unseen fault type. In particular, Siamese networks with contrastive and reconstruction learning are employed to improve feature representation and anomaly detection. Two distinct methodologies are compared: the first utilizes a binary cross-entropy (BCE) loss function to classify the healthy or faulty status of the WT, while the second uses a triplet loss function for multiclass representation learning. Both methodologies generate low-dimensional representations of the input features, also known as embeddings. The resulting feature embeddings are passed through a k-means clustering algorithm to improve fault separation and identification. Statistical features are extracted from SCADA data to capture key trends or event information, while the linear prediction coefficient (LPC) method, which models a signal by predicting future values based on its past samples, is applied to the vibration data for better fault characterization. The proposed approach is evaluated using the publicly available ETH Zurich dataset from an Aventa AV-7 turbine. Experimental results indicate that the fusion of SCADA and vibration-based diagnostics, in combination with contrastive and representation learning, substantially improves the predictive accuracy and generalization of fault detection models.

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.