• Smart Structures and Systems
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• v.22 no.6
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• pp.727-741
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• 2018

To mitigate vibrations, tuned mass dampers(TMD) are widely used for long span bridges or high-rise buildings. Due to some durability concerns, such as fluid degradation, oil leakage, etc., the alternative solutions, such as the non-contacted eddy current damping (ECD), are proposed for mechanical devices in small scales. In the present study, a new eddy current damping TMD (ECD-TMD) is proposed and developed for large scale civil infrastructure applications. Starting from parametric study on finite element analysis of the ECD-TMD, the new design is enhanced via using the permanent magnets to eliminate the power need and a combination of a copper plate and a steel plate to improve the energy dissipation efficiency. Additional special design includes installation of two permanent magnets at the same side above the copper plate to easily adjust the gap as well as the damping. In a case study, the proposed ECD-TMD is demonstrated in the application of a steel arch bridge to mitigate the wind-induced vibrations of the flexible hangers. After a brief introduction of the configuration and the installation process for the damper, the mitigation effects are measured for the ambient vibration and forced vibration scenarios. The results show that the damping ratios increase to 3% for the weak axis after the installation of the ECD-TMDs and the maximum vibration amplitudes can be reduced by 60%.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Steel and Composite Structures
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• v.34 no.5
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• pp.699-713
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• 2020

The aerodynamic performance of long-span cable-stayed bridges is much dependent on its geometrical configuration and countermeasure strategies. In present study, the aerodynamic performance of three composite cable-stayed bridges with different tower configurations and passive aerodynamic countermeasure strategies is systematically investigated by conducting a series of wind tunnel tests in conjunction with theoretical analysis. The structural characteristics of three composite bridges were firstly introduced, and then their stationary aerodynamic performance and wind-vibration performance (i.e., flutter performance, VIV performance and buffeting responses) were analyzed, respectively. The results show that the bridge with three symmetric towers (i.e., Bridge I) has the lowest natural frequencies among the three bridges, while the bridge with two symmetric towers (i.e., Bridge II) has the highest natural frequencies. Furthermore, the Bridge II has better stationary aerodynamic performance compared to two other bridges due to its relatively large drag force and lift moment coefficients, and the improvement in stationary aerodynamic performance resulting from the application of different countermeasures is limited. In contrast, it demonstrates that the application of both downward vertical central stabilizers (UDVCS) and horizontal guide plates (HGP) could potentially significantly improve the flutter and vortex-induced vibration (VIV) performance of the bridge with two asymmetric towers (i.e., Bridge III), while the combination of vertical interquartile stabilizers (VIS) and airflow-depressing boards (ADB) has the capacity of improving the VIV performance of Bridge II.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.759-768
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• 2015

The ground motions of large dimensional structures such as long span bridges at different stations during an earthquake, are inevitably different, which is known as the ground motion spatial variation effect. There are many causes that may result in the spatial variability in seismic ground motion, e.g., the wave passage effect due to the different arrival times of waves at different locations; the loss of coherency due to seismic waves scattering in the heterogeneous medium of the ground; the site amplification effect owing to different local soil properties. In previous researches, the site amplification effects have not been considered or considered by a single-layered soil model only. In this study, however, the ground motion amplification and filtering effects are evaluated by multi-layered soil model. Spatially varying ground motion at the sites with different number of layers, depths, and soil characteristics are generated and the variation characteristics of ground motion time histories according to the correlation of coherency loss function and soil conditions are evaluated. For the bridge system composed of two unit bridges, seismic behavior characteristics are analyzed using the generated seismic waves as input ground motion. Especially, relative displacement due to coherency loss and site effect which can cause the unseating and pounding between girders are evaluated. As a result, considering the soil conditions of each site are always important and should not be neglected for an accurate structural response analysis.