Data-Driven Modeling of Cable Slab Dynamics via Neural Networks

Abstract

A novel method for analyzing the dynamics and bend geometry of a cable slab via trained neural networks is introduced. Neural networks are trained from real-time visual feedback capture via a high-speed camera during cyclic motion to track the positions of multiple markers affixed to the cable slab through image processing techniques. Experimental parameters are systematically varied to ensure a diverse range of training patterns. Consequently, two distinct data-driven neural network models are developed: a coupled model and a decoupled model. These models accurately predict the two-dimensional positions of the markers, even during non-cyclic motion profiles. Subsequently, the marker positions are utilized as waypoints to generate a cubic spline curve with time-varying coefficients, approximating the spatiotemporal solution of the cable slab dynamics. Notably, this spline can be segmented into smaller sections tailored to specific research objectives. Experimental results validate the effectiveness of the proposed methodology.