ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis

Non-linear deformable porous media with sorption (capillary condensation) hysteresis are considered. An artificial neural network with two hidden layers is trained to interpolate the sorption hysteresis using a set of experimental data. The performance of the ANN, which is applied as a procedure in the FE code, is investigated, both from numerical, as well as from physical viewpoint. The ANN-FE code has been developed and tested for 1-D and 2-D problems concerning cyclic wetting-drying of concrete elements. In general, the application of the ANN procedure inside the classical FE program does not have any negative effect on the numerical performance of the code. The results obtained indicate that the sorption isotherm hysteresis is of importance during analysis of hygrothermal and mechanical behaviour of capillary-porous materials. The most distinct differences are observed for the saturation and displacement solutions. The ANN-FE approach seems to be an efficient way to take into account the influence of hysteresis during analysis of hygro-thermal behaviour of capillary-porous materials.