Stay cable tension estimation of cable-stayed bridge under limited information on cable properties using artificial neural networks

This paper proposes a framework for estimating tensions of stay cables of cable-stayed bridge with and without lateral attachments under limited information on cable properties. For stay cables without lateral attachments, tension is estimated using three known parameters, namely, cable length, mass per unit length, and measured frequency while still accounting for unknown features like cable bending stiffness, axial stiffness, cable inclination, and restraint boundary conditions at the cable ends. The methodology is then extended to stay cables with lateral attachments (dampers and cross ties), whose properties are also considered as unknown parameters. The framework is proposed via the application of back-propagation artificial neural networks (ANNs). The finite difference formulation of the cable model is derived to create datasets for training, validation, and testing in the ANNs scheme. The feasibility and robustness of the proposed framework were confirmed through numerical verifications. The results indicated that tension in cables without lateral attachments was successfully evaluated using at least two measured frequencies, whereas cables with lateral attachments needed at least three measured frequencies to achieve high accuracy. Finally, the proposed framework was applied to estimate tensions in stay cables of Tatara Bridge, the longest cable-stayed bridge in Japan. The results demonstrated that the proposed method could estimate the cable tension with acceptable accuracy.

Automated summary

This paper proposes a framework for estimating tensions of stay cables of cable-stayed bridge with and without lateral attachments under limited information on cable properties. The framework is proposed via the application of back-propagation artificial neural networks (ANNs). The feasibility and robustness of the proposed framework were confirmed through numerical verifications.