Vibration-based cable condition assessment: A novel application of neural networks

Abstract Vibration-based cable tension estimation methods demand complex computations especially when usage of comprehensive cable models is required. Avoiding mathematical calculations, this paper proposes a simple novel framework to estimate the cable tension based on Artificial Neural Networks (ANNs). Employing a comprehensive cable model, a set of data including cable length, cable mass per unit length, cable axial stiffness, cable bending stiffness, cable tension and the corresponding cable natural frequencies is generated for training, validation, and testing of the ANNs. The acquired ANNs are then used to estimate the cable tensions in new Ironton-Russell Bridge and the results are compared against the cable tensions directly measured by lift-off test. It will be shown that for new Ironton-Russell Bridge, using cable length, cable mass per unit length, cable axial stiffness, and first two cable natural frequencies as input features to ANNs, the cable tensions can be accurately estimated.