Different approaches to calibration of nonlinear mechanical models using artificial neural networks

Last decades witness rapid development in numerical modelling of structures as well as materials and the complexity of models increases quickly together with their computational demands. Despite the growing performance of modern computers and clusters, calibration of such models from noisy experimental data remains a nontrivial and often computational exhaustive task. The layered neural networks thus represent a robust and effi cient technique to overcome the timeconsuming simulations of a calibrated model. The potential of neural networks consists in simple implementation and high versatility in approximating nonlinear relationships. Therefore, there were several approaches proposed to accelerate the calibration of nonlinear models by neural networks. This contribution reviews and compares three possible strategies based on approximating (i) model response, (ii) inverse relationship between the model response and its parameters and (iii) error function quantifying how well the model fits the d ata. The advantages and drawbacks of particular strategies are demonstrated on calibration o f four parameters of affi nity hydration model from simulated data as well as from experimental measurements. This model is highly nonlinear, but computationally cheap thus allowing its calibration without any approximation and better quantification of results obtained by the examine d calibration strategies.