Basic study on estimation of load bearing performance using deep learning and point clouds of civil infrastructures

Abstract

In the maintenance of civil infrastructures, the importance of efficiently evaluating the load bearing per- formance through numerical analysis is increasing. In this study, we proposed a method to efficiently eval- uate the load bearing performance of civil infrastructures using point clouds, which have become increas- ingly measured and accumulated in recent years. A deep learning network was specifically designed to estimate load bearing performance indices from geometrical features of point clouds. The network was trained using a virtual point cloud dataset of a simple beam of H-steel. We proceeded to estimate the mo- ment of inertia and yield load as the load bearing performance to compare the estimated results with the true values calculated from the actual size. The results show that the moment of inertia and yield load can be estimated with an error of approximately 20% of the true values, and this method is applicability to estimate load bearing performance.