Publications / 2016 Proceedings of the 33rd ISARC, Auburn, USA

Automated 3D Image-Based Section Loss Detection for Finite Element Model Updating

Kasra Ghahremani, Ali Khaloo and David Lattanzi
Pages 403-411 (2016 Proceedings of the 33rd ISARC, Auburn, USA, ISBN 978-1-5108-2992-3, ISSN 2413-5844)

Accurate and rapid condition assessment of in-service infrastructure systems is critical for system-wide prioritization decisions. One major assessment consideration is structural section loss due to deterioration, for instance from corrosion. Modern 3D imaging, which generates high-resolution 3D point clouds, is capable of measuring this degradation. Despite growth in the fields of point cloud analysis, few studies have addressed the potential of using such data for creating and updating numerical finite element models of structures. This paper presents a framework for automatic and systematic 3D section loss detection in structural components, followed by a corresponding update to a finite element model. Point cloud data of a targeted structure is obtained by using recently developed Dense Structure from Motion (DSfM) algorithms. Section loss damage is then located and identified by using computer vision techniques. In order to preserve data integrity and resolve localized high fidelity details, direct 3D point cloud comparisons are applied instead of 3D surface reconstruction or curve fitting techniques that limit the accuracy of the structural analysis. An experimental case study validating the developed approach is presented, along with a discussion of potential uses for the analysis framework. This study aims to prototype a method for fast and reliable detection, structural model updating, and tracking of deterioration in structures, for use by infrastructure managers and engineers. The proposed methodology will enable engineers to use the updated structural model to determine the reserved capacity and remaining service life of structural elements in both in-service structural systems and under severe loading conditions.

Keywords: 3D imaging, Condition assessment, Computer vision, Damage detection, Finite element model, Computational mechanics, Structural damage