Identifying the degree of mode localization in Cross-tied cable networks by using energy-based approach

The use of cross-tie is one of the effective countermeasures to suppress the undesired cable vibration in the cable-stayed bridges. The major benefits offered by the cross-tie solution are the increase in the in-plane stiffness and the flow of energy toward the higher order nodes. However, the formation of closely spaced local modes is one of the major disadvantages of the Cross-tied cable networks. There are only few studies available to understand the formation of local modes. In the current study, an energy-based approach is developed to differentiate between the global and the local modes. In the proposed approach, the kinetic energy equations are formulated to compute the energy stored in arbitrary cable segments. In the current study, the advantages of the proposed energy-based approach over the existing amplitude-based approach have been discussed. The suggested approach has been applied to multiple cable networks, and a comparison has been drawn between the amplitude-based approach and the proposed energy-based approach.

Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy

Attributed to the explosive adoption of large-span spatial structures and infrastructures as a critical damage-sensitive element, there is a pressing need to monitor cable vibration frequency to inspect the structural health. Neither existing acceleration sensor-utilized contact methods nor conventional computer vision-based photogrammetry methods have, to date, addressed the defects of lack in cost-effectiveness and compatibility with real-world situations. In this study, a state-of-the-art method based on modified convolutional neural network semantic image segmentation, which is compatible with extensively varying real-world backgrounds, is presented for cable vibration frequency remote and visual monitoring. Modifications of the underlying network framework lie in adopting simpler feature extractors and introducing class weights to loss function by pixel-wise weighting strategies. Nine convolutional neural networks were established and modified. Discrete images with varying real-world backgrounds were captured to train and validate network models. Continuous videos with different cable pixel-to-total pixel (C-T) ratios were captured to test the networks and derive vibration frequencies. Various metrics were leveraged to evaluate the effectiveness of network models. The optimal C-T ratio was also studied to provide guidelines for the parameter setting of monitoring systems in further research and practical application. Training and validation accuracies of nine networks were all reported higher than 90%. A network model with ResNet-50 as feature extractor and uniform prior weighting showed the most superior learning and generalization ability, of which the Precision reached 0.9973, F1 reached 0.9685, and intersection over union (IoU) reached 0.8226 when utilizing images with the optimal C-T ratio of 0.04 as testing set. Contrasted with that sampled by acceleration sensor, the first two order vibration frequencies derived by the most superior network from video with the optimal C-T ratio had merely ignorable absolute percentage errors of 0.41% and 0.36%, substantiating the effectiveness of the proposed method.

Optimal Design of Dampers for Multi-Mode Cable Vibration Control Based on Genetic Algorithm

Cables in cable-stayed bridges are subjected to the problem of multi-mode vibrations. Particularly, the first ten modes of long cables can have a frequency less than 3[Formula: see text]Hz and hence are vulnerable to wind-rain induced vibrations. In practice, mechanical dampers are widely used to mitigate such cable vibrations and thus they have to be designed to provide sufficient damping for all the concerned vibration modes. Meanwhile, the behaviors of practical dampers are complicated and better to be described by mechanical models with many parameters. Furthermore, additional mechanical components such as inerters and negative stiffness devices have been proposed to enhance the damper performance on cables. Therefore, it is increasingly difficult to optimize the damper parameters for suppressing multi-mode cable vibrations. To address this issue, this study proposes a novel damper design method based on the genetic algorithm (GA). The procedure of the method is first introduced where the damper performance optimization is formulated as a single-objective multi-parameter optimization problem. The effectiveness of the method is then verified by considering a viscous damper on a stay cable. Subsequently, the method is applied to optimize three typical dampers for cable vibration control, i.e. the positive stiffness damper, the negative stiffness damper, and the viscous inertial mass damper. The results show that the GA-based method is effective and efficient for cable damper design to achieve best multi-mode control effect and it is particularly useful for dampers with more parameters.