【Abstract】　

Recently, machine learning (ML) has been actively applied to various problems in the civil engineering field. To further facilitate the use of machine learning in the civil engineering field, it is essential to have appropriate benchmark problems and training datasets with the characteristics of the civil engineering field. Nevertheless, such datasets have not yet been proposed sufficiently. In this study, using the finite element analysis, we propose fundamental datasets (no noise and no missing data) as benchmark problems for damage identification of bridge model, with four levels of difficulty.  Then, we input the dataset into a total of 19 machine learning algorithms to assess the quality of the dataset using the coefficient of determination obtained from those algorithms. As a result of numerical experiments, the following points were found: For cases with a single damaged member, most of the algorithms have the coefficient of determination higher than 0.9, resulting in unsuitable benchmarks due to its simplicity. For cases with two damaged members, the coefficient of determination is distributed from 0.5 to 0.9, resulting in suitable benchmarks with appropriate difficulty.

【Keyword】　

Machine Learning, Benchmark, Dataset, FEM, Bridge, Damage Identification

【Products】

COMSOL Multiphysics,structural-mechanics Module

【Information】