Adversarial Media-Fusion Approach To Strain Prediction For Bridges

This paper contributes to the wide acceptance of autonomous health monitoring for real bridges. Our approach involves dynamic simulation, whereby damage may be identified by detecting abnormal mechanical behavior in the bridge components in response to passing vehicles. Conventionally, dynamic simulation requires expert knowledge of mechanics, components, materials, and structures, in addition to accurate modeling. Moreover, it requires detailed specification of the external forces applied, such as vehicle speeds, loci, and axle weights. This paper introduces a novel media-fusion framework to obtain a bridge dynamic model in a fully data-driven fashion. The proposed generative model also successfully simulated strain responses for a real road bridge by using a camera and strain sensors on the bridge. The generative network was trained by an adversarial learning algorithm customized for media-fusion analysis.