Abstract
Salt crystallisation can induce high stresses in the microstructure of building materials constituting historic buildings and monuments. This dissertation combines numerical methods at different scales to model and analyse the mechanical effects induced by salt crystallisation in porous building materials. Two different scales are considered: the macro-scale, in which the porous building material is seen as a homogeneous continuum, and the micro-scale, being the scale in which can be distinguished the material matrix and the pores. Regarding the macro-scale, a new Hygro-Thermo-Chemical (HTC) model is presented. The HTC model presented is specialised for sodium sulphate solutions and sodium chloride solutions. Moreover, an enriched version of the HTC model is presented in order to describe different drying kinetics, taking into account the kind of efflorescence formation. The HTC modelling results show a good agreement with the experimental data, proving the effectiveness of the proposed model. Concerning the micro-scale, a micro-mechanical model is developed on the base of the real 3D micro geometry of a porous material, coming from X-ray Micro Computed Tomography images. Some hypotheses on the loading condition of the micro-mechanical model, accounting for different crystallisation physics, are introduced. It is shown that the micro-mechanical loading scheme adopted influences the macro-scale mechanical effects and that some approaches commonly used in the literature for their evaluation can lead to their underestimation. In order to establish a link between the micro and the macro scales, a multi-scale approach, based on numerical homogenisation, is presented. It allows to predict the effects of salt crystallisation occurring at the scale of the structure. Finally, the results of the proposed approach are incorporated in a Hygro-Thermo-Chemo-Mechanical model, which combines hygro-thermo-chemical aspects and the mechanical ones, to perform a structural computation with environmental and mechanical loadings to forecast the most probable damage scenarios.
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