Beam Damage Assessment Using Natural Frequency Shift and Machine Learning.

ABSTRACT: Damage detection based on modal parameter changes has become popular in the last few decades. Nowadays, there are robust and reliable mathematical relations available to predict natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF), and the artificial neural network (ANN), as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessmen

SUBMITTER: Gillich N 

PROVIDER: S-EPMC8839218 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature