Structural Control & Health Monitoring最新文献

Bridges in areas with high seismic risk are constantly exposed to earthquake threats. Therefore, comprehensive bridge damage assessments are essential for postearthquake retrofitting and safety assurance. However, traditional methods of assessing damage and collecting data are time-consuming and labor-intensive. To address this issue, this study proposes a deep generative adversarial network (GAN)-based approach to predict the surface damage patterns of bridge columns. Using visual patterns from experimental tests, the proposed approach can generate surface damage to the synthetic column, such as cracks and concrete splinters. The study also investigates the effects of different data representation schemes, such as grayscale, black and white, and obstacle-removed images, and uses the corresponding damage indices as additional constraints to improve network training. The results show that the proposed approach can offer a reliable reference for bridge engineers to evaluate and repair seismic-induced bridge damage, which can significantly lower the cost of disaster reconnaissance.

The identification and classification of railway turnout faults are essential for guaranteeing train safety. Traditional diagnostic methods for these faults face challenges due to limited accuracy, stemming from the scarcity of fault samples, and often fail to provide detailed fault classification. In response to these issues, we introduce an advanced two-stage model for the classification of railway turnout faults, utilizing the FastDTW algorithm, known for its efficient approximation of DTW (dynamic time warping) with linear time and space complexity. In the first stage, we employ a Shapelets feature extraction algorithm, based on a greedy strategy, to efficiently identify the most representative segments from long sequence action curves. Progressing to the second stage, the model tackles the inherent singularities in the FastDTW algorithm by incorporating a novel curve segmentation technique, also rooted in a greedy strategy. This technique fine-tunes the fault classification process, leading to more accurate outcomes. The effectiveness and precision of our proposed model were validated empirically using a dataset of 540 faulty curves from a specific high-speed railway station, achieving an impressive classification accuracy of 97%. This substantial accuracy in fault curve classification underscores the potential of our model to significantly enhance the safety and efficiency of railway operations, marking a notable advancement in the field of railway turnout fault classification.

Methods of structural health monitoring (SHM) are often challenged by changing environmental and operational conditions (EOC). This paper presents a novel experimental testing field specifically designed for studying the effects of EOC on black box vibration-based output-only SHM methods. The experimental testing field consists of two identical mechanical structures that are exposed to mass and stiffness perturbations: one in a controllable laboratory setup and one under the influence of varying EOC in a field setup. The paper demonstrates the feasibility and usefulness of the dual experimental testing field for studies about EOC influences on SHM. The results of a preliminary study of the occurring EOC in the field setup are presented, and a modular measurement system that provides high-quality data is introduced. By providing the experimental acceleration data, a new experimental benchmark dataset for various studies and future use in the field of SHM is presented.

Timber structures are vulnerable to failure and collapse under seismic action. To improve the seismic performance of such structures, a replaceable displacement amplification rotary friction damper was proposed and designed. Six specimens were fabricated, each varying in pretension strains and employing three different composite friction materials as control parameters, followed by low cyclic loading tests. The study investigated the working mechanism, hysteresis performance, energy dissipation capacity, performance stability, and displacement amplification effect of the dampers. A finite element model was developed to analyze the hysteresis performance of the damper and evaluate the impact of various parameters on its overall effectiveness. Furthermore, a comparative analysis of the damper’s hysteresis characteristics was conducted. The theoretical calculations and finite element analysis were validated using experimental results, showing a relative error within 10%. The specimens demonstrated a notable displacement amplification capability, which increased as the intermediate connector length decreased. By reducing the length by 200 mm, the maximum damping force could be amplified by 5.5 times, while the nodal rotation values increased by 3.92 times. Additionally, for every 50 με increment in pretension strain, energy consumption increases by an average of 148%, and for each unit increase in the friction coefficient, energy consumption increases by an average of 172%.

Dynamic displacement is an important indicator for bridge safety estimation but is difficult to measure due to economic or technological limitations. Dynamic responses of a passing vehicle include the bridge dynamic response information. This study proposes a framework utilizing artificial neural networks to efficiently and accurately predict bridge displacements from the dynamic response of a passing vehicle. The input and the output of the networks are the vehicle acceleration responses and the bridge dynamic displacement responses, respectively. The implemented framework consists of convolutional neural network (CNN) and gated recurrent units (GRU). CNN is adept at feature extraction in the microcosm of short-term time series, revealing intricate nuances. As a complement, GRU plays a crucial role in extracting features of macroscopic long-term time series. The CNN-GRU network can efficiently extract higher-order features contained in the input data. Numerical experiments are conducted using the developed vehicle-bridge interaction (VBI) system model to obtain requisite data for training the deep neural network. The impact of the presence or absence of roadway irregularities and the number of vehicles are discussed, indicating the accuracy of the framework. Furthermore, a laboratory experiment is conducted to further assess the performance of the CNN-GRU network. Results indicate that the CNN-GRU network offers an effective alternative for bridge displacement measurements.

Persistent Scatterer Interferometry (PSI) is a remote sensing measurement technology based on electromagnetic waves, capable of simultaneously monitoring deformations in large urban building complexes with millimeter-level precision. However, relying solely on the line of sight (LOS) deformation sequence based on a specific permanent scatterer cannot accurately analyze building deformations, particularly in cases where high-rise buildings may simultaneously experience various deformation components such as temperature-induced deformation, shrinkage, creep, and tilting. To accurately identify the deformation states of high-rise buildings, the paper begins by systematically summarizing three typical deformation patterns from the unique perspective of synthetic aperture radar (SAR) satellites. These patterns include Pattern I, characterized by temperature-induced deformation alone, and Pattern II and Pattern III, which involve a combination of deformation in different directions relative to the SAR satellite in addition to temperature-induced deformation. To accurately monitor the LOS deformation of high-rise buildings, the paper introduces the concept of acquiring the evolutionary trends of temperature-related deformation coefficients and proposes a methodology for recognizing and quantifying deformation in high-rise buildings. Subsequently, this study utilized freely available Sentinel-1 satellite data to observe the deformation of nine high-rise buildings in Changsha, China. The research findings indicate that the thermal expansion coefficients of most high-rise buildings fall within the range of 6 ∼ 12 × 10⁻⁶/°C. High-rise buildings that have been constructed for more than ten years almost no longer experience significant shrinkage or creep, while new constructions may exhibit an initial shrinkage and creep of up to 1.2 × 10⁻⁴ mm/mm. Additionally, the study results demonstrate that super-tall buildings may exhibit centimeter-scale lateral deformations at their tops due to uneven shrinkage. Findings from the study indicate that the proposed method can achieve cost-effective and sustainable deformation monitoring of high-rise building clusters within a large urban area.

In the process of infrastructure construction in recent decades, there exist millions of bridges in service that need safety inspection for performance assessment. Currently, computer vision and deep learning-based surface damage detection methods can achieve classification and localization of damages at the image level, but achieving precise localization in three-dimensional space is more challenging. To overcome aforementioned limitations, this study proposes a three-stage method of bridge surface damage detection and localization based on three-dimensional (3D) reconstruction. In stage 1, the UAV flight path planning of the bridge is carried out, and the 3D reconstruction model of the bridge is formed based on the structure from motion (SfM) algorithm. In stage 2, you-only-look-once version 7 (YOLOv7) network is adopted to detect multiple damages, and scale invariant feature transform (SIFT) detector is used to match the identical damage in image level. In stage 3, based on solution of epipolar geometric constraint, the matched damage was mapped to the 3D model, and the 3D damage localization is realized and visualized. The quality of the 3D model has been analyzed, and it is recommended that inspection distance is determined at 20 m. Moreover, the reconstruction model of bridges achieves centimeter-level positioning accuracy, and the positioning accuracy of damage reaches the meter level. The mapped model effectively showcases surface damages, providing bridge owners with intuitive insights.

The parameters of most conventional passive dampers are constant, which may not sufficiently satisfy the different energy dissipation capacity demands of the structure under different load conditions. The development of passive dampers with variable performances has become an emerging and vital trend in energy dissipation technologies and smart structures. This study proposes a novel passive viscous fluid damper with variable performance under different deformation levels called an asynchronized parallel double-stage viscous fluid damper (APDVFD). It is expected to exhibit an asynchronized double-stage working mechanism based on a variable annular gap. In the first stage, only the primary piston provides the damping force. When the deformation reaches a preset value, the primary and second pistons work in parallel, providing a damping force concurrently. Circular orifices are adopted for the piston head to provide a sufficient damping force. The double-stage operating mechanism and fatigue performance of the APDVFD were validated and investigated through a full-scale experiment with 46 load cases. Based on these, a theoretical model capable of predicting the hysteretic behavior of the APDVFD was developed and validated against test data.

The temperature dependence of the internal structure and elastic properties of concrete is revealed by subjecting concrete to different heating conditions. The variation trend of wave velocity in the concrete medium with temperature is analyzed through ultrasonic properties. The decrease in cement matrix modulus and the increase in crack density in concrete are the main factors leading to a decrease in wave velocity. The changes in the composition of the concrete matrix after dehydration are obtained using a thermal decomposition model. Based on the effective medium model, the calculation results of the effective modulus at different temperatures are presented, with a focus on analyzing the influences of the temperature-dependent changes in the matrix elastic properties and the randomly distributed cracks on the effective modulus. The experimental tests and the presentation of the model results indicate a relatively satisfactory agreement, thereby verifying the reliability of the models. The results of this study can explain the basic propagation mechanism of waves in concrete and have promising applications in the ultrasonic testing of thermal damage to cement-based materials.

The negative stiffness damper (NSD) has emerged as a promising passive vibration control device for cable structures due to its simplicity and effectiveness. However, uncertainties stemming from both internal and external environmental factors can potentially compromise the NSD’s performance in real-world applications, posing risks to cable safety. In response, this paper conducts a robustness evaluation on an integrated cable-NSD system, taking into account various potential uncertainties. Specifically, the uncertain parameters are described by interval variables. Consequently, an interval model is constructed to delineate the boundaries of cable dynamic responses when subjected to these uncertainties. The model’s accuracy is validated against experimental results. Subsequent simulations involve assessing interval responses for both single- and multimode cable vibrations under varying uncertainties. Finally, the NSD’s robustness concerning cable vibration control is evaluated using the model, which incorporates the first-passage theory. This analysis delves into the relationships among confidence levels, performance measures, and the variation range of uncertainties. The results indicate that for single-mode vibration control, there is a 90% confidence level that the damping ratio reduction remains within 10%. As for multimode vibration control, a 90% confidence level is established that the amplification falls within 17%.