Structural Control & Health Monitoring最新文献

In long-period structures, rate-independent linear damping (RILD) offers superior control of acceleration responses compared with conventional viscous damping. However, the noncausality of RILD limits its application in buildings. Further, practical implementation of methods to approximate RILD in specific situations remains challenging owing to the difficulty of mechanical realization. In this study, the inerter, Maxwell–Wiechert model, and negative stiffness were employed to construct novel RILD-approximating devices: the negative-stiffness inerter Maxwell–Wiechert (NSIMW) model and the NSI-spring Maxwell–Wiechert (NSISMW) model. These devices were designed to replicate the storage and loss stiffness of RILD at the target frequencies. This study proposes causal devices for approximating RILD using inerters, negative stiffness, and Maxwell–Wiechert models. The initial design—the negative-stiffness viscous-inerter Maxwell–Wiechert (NSVIMW) model—was unrealizable owing to negative design parameters. To resolve this, the viscous damper was removed, resulting in a simplified NSI Maxwell–Wiechert (NSIMW) model. An enhanced version of the NSISMW model was further developed by adding a spring element. Both models were designed to match the storage and loss stiffness of RILD at target frequencies, and direct design methods were developed to determine their parameters. Real-time hybrid simulations and analytical analyses were conducted to evaluate seismic control performance. The findings demonstrate that while NSIMW provides a causal approximation of RILD with moderate effectiveness, the NSISMW model achieves a more satisfactory seismic performance than the RILD, negative-stiffness Maxwell–Wiechert model, and traditional tuned viscous mass dampers. These results clarify the relative merits of the two designs and suggest that the NSISMW model offers a promising direction for the practical implementation of RILD-inspired damping systems.

Engineering structures usually have rare damage scenarios, which provide insufficient labels to identify the structural damage. Therefore, the effectiveness and accuracy of traditional data-driven methods with limited training samples is an urgent task. This study develops a semisupervised transfer learning (TL) method for structural damage identification. The effectiveness is validated through experiments on a scale cable-stayed bridge specimen. Initially, a convolutional neural network (CNN) was trained based on a numerical dataset simulated by a finite element model, where a source domain is modeled with strong recognition performance and generalizability. Subsequently, the network structure and hyperparameters in the source domain were transferred to the corresponding positions in the experimental training model to create a pretrained model in the target domain; this pretrained model was updated by using site-specific measured data from the engineering structure in the target domain. Finally, a dynamic threshold self-training algorithm was employed to further optimize the model: in the initial stage, a high-confidence threshold of 90% was set to filter reliable pseudolabels, and the threshold decreases to 80% gradually, under the condition that the model cannot provide predictions with sufficiently high confidence. Experimental study on a scale cable-stayed bridge specimen was conducted to demonstrate the effectiveness of the proposed method. Four networks were established based on (1) experimental samples; (2) both labeled and unlabeled experimental samples with the self-training algorithm; (3) TL from the finite element model and experimental samples; and (4) applying self-training with unlabeled samples to the target model derived from TL. The results indicate that the damage identification accuracy of the aforementioned models is 73.6%, 79.4%, 84.6%, and 89.8%, respectively. The TL improves the reliability of generating pseudolabels by utilizing the self-training process and unlabeled data and decreases errors in pseudolabels. Both finite element simulation data and practical unlabeled sample data were successfully combined by the TL and self-training semisupervised learning method for damage identification in cable-stayed bridges effectively.

Optimizing the mesoscopic structure of concrete based on energy dissipation theory is an effective approach to enhancing its performance. However, related works are limited, and the mathematical relationship between the mesoscopic structure and the energy dissipation mechanism remains unclear. In this study, image processing technology and MATLAB were employed to quantitatively characterize the mesoscopic structure of concrete. A mesoscopic model of concrete was constructed using PFC2D to study the influence of mesoscopic parameters on energy dissipation, and a mathematical model of energy dissipation incorporating mesoscopic parameters was fitted using a deep neural network. In addition, the genetic algorithm with associated individuals (GA-AIs) was applied to optimize the mesoscopic parameters. The main findings are as follows: (1) Based on the entropy-containing random aggregate method, Fourier shape reconstruction analysis, and density-damping random field method, the aggregate spatial arrangement, shape characteristics, and mortar matrix characteristic property were quantified separately. (2) Under uniaxial compression, the three types of mesoscopic parameters showed positive correlations to varying degrees with the energy dissipation capacity of concrete. By adjusting these parameters, the energy dissipation capacity can be effectively modulated. (3) The GA-AIs efficiently optimized the mesoscopic parameters, enabling effective control of the energy dissipation capacity of concrete. Based on the optimization results under a specific working condition, the algorithm can infer mesoscopic parameter values to meet different performance requirements for this condition.

As aging bridge infrastructure poses increasing safety risks, there is a critical need for reliable and scalable Structural Health Monitoring (SHM) systems. Traditional SHM methods, which rely on fixed sensor networks and assessments of individual bridges, face significant challenges in scalability, cost, and efficiency—particularly in complex urban environments. To address these limitations, this study introduces the Multibridge Inference SHM (MISHM) framework. MISHM leverages drive-by monitoring and crowdsensing to observe multiple bridges simultaneously. It employs a feature-based analysis using Mel-frequency cepstral coefficients (MFCCs) and Kullback–Leibler (KL) Divergence to identify structural changes. Here, “inference” refers to drawing conclusions about the health of each individual bridge by comparing patterns and features gleaned from the entire network, rather than relying on isolated measurements. By making multiple comparisons across all monitored structures, MISHM enhances fault tolerance, reduces missed detections, and offers a scalable solution for smart city infrastructure monitoring. This framework represents a vital advancement in SHM systems, addressing the evolving needs of large-scale urban infrastructure management.

The impact of pit excavation on the surrounding environment is closely related to the deformation characteristics of the surrounding enclosure structure. However, most existing methods rely on calculating pit unloading stress based on the Mindlin solution, which does not adequately account for the dynamic deformation characteristics of the enclosure structure at different excavation stages and is difficult to apply for real-time assessment. This paper presents a new calculation method based on the mobilizable strength design (MSD) approach to dynamically predict the horizontal displacement of the shield tunnel adjacent to the excavation pit. By introducing dynamic evaluation of the horizontal displacement of the enclosure structure, the applicability of the traditional MSD method is enhanced. The paper compares and analyzes the differences between this method, the modified MSD (MMSD) method, the MSD method, and measured data from actual pit excavation cases. The results demonstrate that the proposed method more accurately reflects the deformation characteristics of the enclosure structure at different excavation stages and its dynamic impact on the horizontal displacement of the shield tunnel. The spatial distribution of horizontal displacement in the enclosure structure under zoned excavation is analyzed, revealing the coupling relationship between the deformation characteristics of the enclosure structure and the tunnel’s deformation response. The findings of this study provide valuable references for the safety assessment and protective measures of shield tunnels during pit excavation.

Accurate prediction of deformation under thermal influences is critical for the safety assessment and long-term performance of high dams. This study proposes a novel two-stage prediction framework that integrates statistical modeling with deep learning to enhance the interpretability and accuracy of dam deformation forecasting. In the first stage, sparse principal component analysis (sPCA) is employed to extract dominant features from high-dimensional thermometer data. These features are then used to construct an interpretable dam deformation monitoring model using multiple linear regression (MLR), referred to as the HT_sPCAT-MLR model. In the second stage, the multilayer bidirectional gated recurrent unit (multi-Bi-GRU) network is developed to model the residuals of the HT_sPCAT-MLR framework, leveraging advanced gating mechanisms and bidirectional temporal learning to improve long-term prediction accuracy. Furthermore, the adaptive genetic algorithm (AGA) is utilized to optimize the hyperparameters of the multi-Bi-GRU model, enhancing the robustness and generalization of the residual correction module. The proposed methodology is validated using real-world monitoring data from an ultra-high arch dam. Quantitative evaluation at four representative measurement points shows that the proposed model consistently outperforms baseline methods across all key metrics. Specifically, it achieves R² values above 0.99, mean absolute error reductions of over 80% compared to traditional models, and the lowest sMAPE across all cases. The experimental results demonstrate model’s superior prediction accuracy, robustness, and practical applicability for dam deformation. The integrated framework offers a reliable and interpretable solution for thermal deformation forecasting in high dam structures.

The increasing complexity of transportation infrastructure demands advanced, data-driven approaches for early pavement distress detection and maintenance decision-making. Traditional assessment methods often fail to provide reliable, interpretable, and proactive insights into pavement degradation. This study introduces an Explainable Artificial Intelligence (XAI) framework that integrates clustering algorithms with principal component analysis (PCA) to improve early-stage pavement distress analysis. The proposed framework leverages K-means, Gaussian mixture models (GMMs), and hierarchical clustering, applied to a customized dataset encompassing pavement performance metrics, geospatial information, and aggregate properties. By incorporating ground-truth validation, our approach not only differentiates between high-quality and deteriorating pavement sections but also reveals underlying factors contributing to distress, overcoming the opacity of traditional machine learning (ML) models. Results demonstrate that this transparent, interpretable AI-driven framework enhances infrastructure resilience by enabling data-informed decision-making for predictive maintenance. Beyond transportation engineering, the methodology establishes a scalable paradigm for explainable AI applications in civil infrastructure, advancing the intersection of ML, geospatial analysis, and material science.

This article proposes a new approach to interface both numerical and experimental substructures of a real-time hybrid simulation experiment running at different sampling rates. A regularized Wiener statistical finite impulse response filter is applied to the slow-rate sequence of interface target displacements at the numerical substructure to predict the next data point. Then, an interpolation rule using monomials is applied to obtain the sequence of interface target displacements at the experimental substructure running at a fast sampling rate. The Wiener filter is trained using offline simulations of the partitioned reference structure before the main experiment. The proposed scheme achieves good results for virtual simulations with linear and nonlinear structures, and it separates the task of determining simulation rates between substructures, ensuring both accuracy and stability in the experimental test.

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.