Structural Control & Health Monitoring最新文献

Timely wear evaluation is crucial in maintaining the functionality of bridge expansion joints (BEJs), ultimately ensuring the safety of bridges. Despite the significance of traffic load simulation (TLS) in simulation-based evaluation methods, existing TLS approaches face challenges in accurately modeling in situ traffic flow at a high fidelity. This paper presents a novel methodology and its application for evaluating the wear performance of BEJs, employing a Transformer-enhanced TLS approach. Initially, a tailored dataset is crafted for data-driven car-following modeling, leveraging an established spatial-temporal traffic load monitoring system. High-fidelity TLS with a mean absolute error (MAE) of 0.1738 m/s is then achieved using Transformer modules equipped with an attention mechanism. To evaluate the final wear life of BEJs, transient dynamic analysis and a calibrated finite element model of the bridge are employed to extract cumulative displacement. Additionally, a surrogate model is developed to depict the relationship between the hourly traffic weight on the entire bridge deck and the cumulative displacement of BEJs, yielding an impressive R-squared value of 0.96619. Comparative results demonstrate the superior performance of our proposed TLS approach over other data-driven approaches, with the linear model derived from our TLS approach outperforming the model generated by the conventional Monte Carlo-based TLS approach. To conclude, our proposed TLS emerges as a comprehensive and precise methodology for the wear evaluation of BEJs.

This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known.

A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart aggregate (SA)-based concrete stress monitoring. First, the framework of the SA-based stress monitoring using deep learning of raw impedance signals is described. An impedance measurement model is designed for a SA-embedded concrete body under compression. A 1D CNN model is developed for deep learning of raw impedance signals corresponding to various stress levels. Three approaches for concrete stress monitoring are designed to deal with data availability, signal noises, and untrained stress levels. Second, a few SA-embedded concrete cylinders are experimented to measure impedance signals under various stress levels. Finally, the performance of the proposed method is extensively evaluated by investigating the feasibility of the K-fold cross-validation to deal with the data availability and the effects of signal noises and untrained data on the accuracy of stress estimation in the SA-embedded concrete cylinders.

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

Seismic isolation has become a widely accepted method for the protection of structures and nonstructural components. However, this control strategy is unfavorable against near-fault earthquakes, particularly those featuring velocity-pulse effects. Excessive isolation displacements and accelerations can occur during such earthquakes, resulting in amplified responses of the superstructure. To resolve this problem, this study develops a prototype of the eccentric rolling isolation system consisting of one platform eccentrically pin-connected to four circular rollers. The eccentric pin connection yields a nonlinear restoring force of the proposed system and results in displacement-dependent resonances, and the inherent mechanical friction also yields an energy dissipation capability of the system. As the magnitude of ground excitation increases, the prototype system generates a lower resonant frequency away from the dominant frequencies of earthquakes. This behavior is experimentally investigated and verified for mechanical behavior and seismic performance. In the experiment, sinusoidal, far-field, and near-fault ground motions are considered in shaking table testing. Some parameters, such as the eccentricity, roller size, and inertial force, are also experimentally investigated. As found in the experimental result, the feasibility of the prototype system is successfully verified. Meanwhile, the comparable simulation results further validate the mathematical model of the prototype system. Consequently, the eccentric rolling isolation system has demonstrated isolation effectiveness against far-field ground motions and has good potential to perform better than a linear system under near-fault ground motions.

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

A novel methodology for the characterisation of the nonlinear behaviour of post-tensioned r.c. bridges, which exploits the response to heavy traffic travelling during operational conditions, is presented. This type of bridges shows a nonlinear elastic behaviour due to the partial opening of cracks under heavy loads whose entity is related to the intensity of the prestressing force. The properties of this response vary because of material relaxation or damage of the prestressing system. The study exploits the abilities of the Hilbert–Huang transform (HHT) to extract the instantaneous properties of the dynamic response, and a novel procedure to characterise the nonlinear elastic response is presented and investigated through theoretical applications on simplified dynamic systems. A frequency-amplitude correlation chart is proposed as a visual tool to retrieve useful information on the nonlinear response related to the instantaneous variation of the natural frequency with the response amplitude. With the aim of denoising and eliminating spurious contributions introduced by the local nature of the information extracted through the Hilbert spectral analysis, a probabilistic model is proposed for the result interpretation, through which the probability distribution of the instantaneous natural frequencies conditional to different levels of the response amplitude is provided and potential bridge’s response modifications and anomalous behaviours of the prestressing system can be detected. An extensive parametric analysis is performed to assess the influence of the most relevant parameters governing the problem and verify the effectiveness of the proposed strategy.

Vortex-induced vibrations (VIVs) have been frequently observed on long-span bridges (LSBs) in recent years. Unlike other destructive aerodynamic phenomena of LSBs, VIVs are self-limited in amplitude, primarily affecting the serviceability of LSBs through unpleasant users’ feelings characterized by human body vibration. Most existing studies discussed this issue based on a popular human body vibration measure, the human comfort index (HCI) in ISO 2631-1. However, the HCI is primarily concerned with vibration above 0.5 Hz, which might be unsuitable for disclosing the influence of VIV because of the low-frequency features of LSBs’ VIVs. To address this limitation, this study advocates using the motion sickness index (MSI) to revisit the serviceability of LSBs experiencing VIVs based on an innovative wind-traffic-bridge simulation platform. Different from current studies exclusively focusing on vehicle riders, this paper additionally incorporates a vibration model for standing persons to understand the feelings of the pedestrians on the bridge. On this basis, the influence of VIV and traffic load is comprehensively examined. The results indicate that the HCI is inappropriate for exploring the serviceability of LSBs under VIVs regarding users’ feelings, but the MSI is a good alternative. Moreover, the increasing traffic load can obviously mitigate the adverse effect of VIVs on the bridge’s serviceability, which may be utilized to control VIVs of LSBs in real-world engineering practice.

The control of angular velocities in the fixed-axis rotation of rigid bodies is crucial for ensuring the safety and functionality of civil structures and mechanical systems. In this research, a novel enhanced torsional eddy current damper (ETECD) is proposed to effectively control the angular velocities of rigid bodies within confined installation spaces. At first, an estimation approach is developed to determine the damping coefficient of the eddy current damper (ECD) within limited installation space. Furthermore, we utilize a gearbox to enhance the damping performance of the ECD in confined spaces. To establish the framework for the design of the proposed ETECD, the motion equation and solution of the rotating body are derived. By analytically presenting the approximate solution for the responses of a rotating body with a torsional viscous damper, the required range of the torsional damping coefficient is derived. This range ensures compliance to velocity restrictions under linearly angle-related torques, guiding the design of the ETECD. The ETECD, comprising two cylindrical torsional eddy current dampers (ECDs) and a motion-amplified gearbox, is designed and tested for a rotating body. Numerical examples and experimental tests are carried out to validate the performance of the proposed ETECD. The calculated damping coefficients and predicted control performance in the numerical examples agree well with the experimental results. Notably, under the minimum and maximum torques, the terminal angular velocity (TAV) of the rotating body can be significantly reduced by 70.76% and 58.99%, respectively. The proposed work emphasizes the potential of the ETECD as an effective and economic method in reducing angular velocities for rotating bodies.

Contemporary multiscale construction object detection algorithms rely predominantly on fully-supervised deep learning, requiring arduous and time-consuming labeling process. This paper presents a novel semisupervised multiscale construction objects detection (SS-MCOD) by harnessing nearly infinite unlabeled images along with limited labels, achieving more accurate and robust detection results. SS-MCOD uses a deformable convolutional network (DCN)-based teacher-student joint learning framework. DCN uses deformable advantages to extract and fuse multiscale construction object features. The teacher module generates pseudolabels for construction objects in unlabeled images, while the student module learns the location and classification of construction objects in both labeled images and unlabeled images with pseudolabels. Experimental validation using commonly used construction datasets demonstrates the accuracy and generalization performance of SS-MCOD. This research can provide insights for other detection tasks with limited labels in the construction domain.