FE modelling of crack width in reinforced concrete beams supported by artificial neural network surrogate models

The concrete cracking is simulated by the finite element method combined with the constitutive model based on the nonlinear fracture mechanics using finite element simulation software.It is known that numerical simulations of reinforced concrete using the finite element method can be strongly influenced by the assumptions of crack spacing or crack band size, especially when large finite element sizes are used.The proposed approach attempts to address this issue by using machine learning and artificial neural network surrogate models to estimate crack spacing in reinforced concrete structures.The model uncertainties for mean and maximum crack widths are evaluated using the database of laboratory results.The reinforcement arrangement, dimensional simplification, and numerical discretization effects on the model uncertainty are investigated.The numerical model offers an adequate prediction of crack widths for the beams with a single-layer reinforcement and exhibits less accuracy for the multilayer bar arrangement.The presented numerical model represents an advanced tool for the crack width assessment in the design of reinforced concrete structures in serviceability limit states.