• Wind and Structures
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• 제4권6호
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• pp.505-518
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• 2001

Shantou Bay Bridge is the first long-span suspension bridge in China. Because of its location near the Shantou Seaport and its exposure to high typhoon winds, wind-resistant studies are necessary to be made. In this paper, critical flutter wind speeds and buffeting responses of this bridge at its operation and main construction stages are investigated. The Buffeting Response Spectrum method is first briefly presented. Then the sectional model test is carried out to directly obtain the critical flutter wind speed and to identify the flutter derivatives, which are adopted for the later analysis of the buffeting responses using the Buffeting Response Spectrum method. Finally the aeroelastic full bridge model is tested to further investigate the dynamic effects of the bridge. The results from the tests and the computations indicate that the flutter and buffeting behaviors of the Shantou Bay Bridge are satisfied.

• Wind and Structures
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• 제23권3호
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• pp.253-273
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• 2016

Ice accretions on stay cables may result in the instable vibration of galloping, which would affect the safety of cable-stayed bridges. A large number of studies have investigated the galloping vibrations of transmission lines. However, the obtained aerodynamics in transmission lines cannot be directly applied to the stay cables on cable-stayed bridges. In this study, linear and nonlinear single degree-of-freedom models were introduced to obtain the critical galloping wind velocity of iced stay cables where the aerodynamic lift and drag coefficients were identified in the wind tunnel tests. Specifically, six ice shapes were discussed using section models with geometric scale 1:1. The results presented obvious sudden decrease regions of the aerodynamic lift coefficient for all six test models. Numerical analyses of iced stay cables associated to a medium-span cable-stayed bridge were carried out to evaluate the potential galloping instability. The obtained nonlinear critical wind velocity for a 243-meter-long stay cable is much lower than the design wind velocity. The calculated linear critical wind velocity is even lower. In addition, numerical analyses demonstrated that increasing structural damping could effectively mitigate the galloping vibrations of iced stay cables.

• Steel and Composite Structures
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• 제19권2호
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• pp.405-416
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• 2015

We proposed the concept of nominal rigidity of a long-span V-shaped rigid frame composite arch bridge, analyzed the effects of structural parameters on nominal rigidity, and derived a theoretical nominal rigidity equation. In addition, we discussed the selection of the arch-to-beam rigidity ratio and its effect on the distribution of internal forces, and analyzed the influence of the ratio on the internal forces. We determined the delimitation value between rigid arch-flexible beam and flexible arch-rigid beam. We summarized the nominal rigidity and arch to beam rigidity ratios of existing bridges. The results show that (1) rigid arch-flexible beam and flexible arch-rigid beam can be defined by the arch-to-beam rigidity ratio; (2) nominal rigidities have no obvious differences among the continuous rigid frame composite arch bridge, V-shaped rigid frame bridge, and arch bridge, which shows that nominal rigidity can reflect the global stiffness of a structure.

• Wind and Structures
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• 제17권3호
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• pp.275-298
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• 2013

This paper presents a number of approximated analytical formulations for the flutter analysis of long-span bridges using the so-called uncoupled flutter derivatives. The formulae have been developed from the simplified framework of a bimodal coupled flutter problem. As a result, the proposed method represents an extension of Selberg's empirical formula to generic bridge sections, which may be prone to one of the aeroelastic instability such as coupled-mode or single-mode (either dominated by torsion or heaving mode) flutter. Two approximated expressions for the flutter derivatives are required so that only the experimental flutter derivatives of ($H_1^*$, $A_2^*$) are measured to calculate the onset flutter. Based on asymptotic expansions of the flutter derivatives, a further simplified formula was derived to predict the critical wind speed of the cross section, which is prone to the coupled-mode flutter at large reduced wind speeds. The numerical results produced by the proposed formulas have been compared with results obtained by complex eigenvalue analysis and available approximated methods show that they seem to give satisfactory results for a wide range of study cases. Thus, these formulas can be used in the assessment of bridge flutter performance at the preliminary design stage.

• Wind and Structures
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• 제32권4호
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• pp.293-308
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• 2021

For the past three decades a significant amount of research has been conducted on bridge flutter. Wind tunnel tests for a 2000 m class twin-box suspension bridge have revealed that a twin-box deck carrying 4 m tall 50% open area ratio wind screens at the deck edges achieved higher critical wind speeds for onset of flutter than a similar deck without wind screens. A result at odds with the well-known behavior for the mono-box deck. The wind tunnel tests also revealed that the critical flutter wind speed increased if the bridge deck assumed a nose-up twist relative to horizontal when exposed to high wind speeds - a phenomenon termed the "nose-up" effect. Static wind tunnel tests of this twin-box cross section revealed a positive moment coefficient at 0° angle of attack as well as a positive moment slope, ensuring that the elastically supported deck would always meet the mean wind flow at ever increasing mean angles of attack for increasing wind speeds. The aerodynamic action of the wind screens on the twin-box bridge girder is believed to create the observed nose-up aerodynamic moment at 0° angle of attack. The present paper reviews the findings of the wind tunnel tests with a view to gain physical insight into the "nose-up" effect and to establish a theoretical model based on numerical simulations allowing flutter predictions for the twin-box bridge girder.

• Wind and Structures
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• 제28권5호
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• pp.331-345
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• 2019

Long-span bridge decks are often shaped as streamlined to improve the aerodynamic performance of the deck. There are a number of important shaping parameters for a streamlined bridge deck. Their effects on aerodynamics should be well understood for shaping the bridge deck efficiently and for facilitating the bridge deck design procedure. This study examined the effect of various shaping parameters such as the bottom plate slope, width ratio and side ratio on aerodynamic responses of single box streamlined bridge decks by employing unsteady RANS simulation. Steady state responses and flow field were analyzed in detail for wide range of bottom plate slopes, width and side ratios. Then for a particular deck shape Reynolds number effect was investigated by varying its value from $1.65{\times}10^4$ to $25{\times}10^4$. The aerodynamic response showed very high sensitivity to the considered shaping parameters and exhibited high aerodynamic performance for a particular combination of shaping parameters.

• Smart Structures and Systems
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• 제29권1호
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Smart Structures and Systems
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• 제29권1호
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Smart Structures and Systems
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• 제32권2호
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Smart Structures and Systems
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• 제29권5호
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• pp.715-728
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• 2022

Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.