Regularization Methods Applied to Noisy Response from Beams under Static Loading

Abstract

The estimation of flexural stiffness from static loading test data is the basis of many methods assessing the condition of structural elements. These methods are usually developed under the assumption of having sufficiently accurate data available. Hence, their performance deteriorates as the differences between the measured and true values of the response, often denoted as noise, increase. The proposed methodology is specifically designed to mitigate errors derived from noisy static data when estimating flexural stiffness. It relies on the linearization of the equations relating displacements to stiffness through the unit-force theorem, combined with regularization tools such as L-Curve and generalized cross-validation. The methodology is tested using theoretical simulations of the static response of a simply supported beam subjected to a 4-point flexural test for several levels of noise, two types of responses (deflections and rotations) and different levels of discretization. Recommendations for selecting the optimal regularization tool and parameter are provided. The use of rotations as inputs for predicting stiffness is shown to outperform deflections. Finally, the methodology is extended to a statically indeterminate beam.