3D Stress Estimation Using Adapted Finite Element Model Updating Techniques

Abstract

According to a 2016 study by the American Road and Transportation Builders Association (ARTBA) one bridge in every ten is structurally deficient. Two major contributors of structural deficiency are corrosion, which causes material loss and thinning of cross sections, and permanent plastic deformations. Currently, there are no standard methods for understanding how measurements of these damages impact stress and capacity analysis. The research presented in this thesis focuses on the use of 3D images to create “point clouds” for such structural capacity analysis. Using a set of previously developed techniques that measure both section loss and deformations in point clouds, two studies were performed to analyze the effectiveness of using these techniques to update corresponding finite element models. The first study was a sensitivity analysis to quantify the effect of image noise on stress concentration estimates, and to better understand the limits of the updating approach. In the second study, point cloud xi deflection measurements from three-point bending tests were used to induce translations and stresses in a finite element model. The results of the first study showed that increasing image noise resulted in a higher likelihood that artifacts would form in the finite element model, leading to a localized increase in stress; however, it was also found that subsurface stresses matched the values expected from elastic theory and methods of analyzing the data with these anomalies are discussed. The findings of the second study showed that applying localized displacements in the 3D finite element model created localized stress concentrations that do not represent the expected stress profiles. While both studies provide important insight into this relatively new technology, future work to be performed might include creating methods to better differentiate between artificial stress anomalies and actual states of stress, as well as experimental validation.