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Deformations in Cement Pastes during Capillary Imbibition and Their Relation to Water and Isopropanol as Imbibing Liquids.

Natalia Mariel AldereteArn MignonKatrin SchollbachYury Villagrán-Zaccardi
Published in: Materials (Basel, Switzerland) (2021)
The traditional approach for evaluating capillary imbibition, which describes the phenomena as a linear relationship between mass gain and the square root of time, considers a rigid pore structure. The common deviation from the linearity when using the square-root law (manifested in a downward curvature, i.e., slower water ingress) can be explained by considering a changing pore structure during the process caused by the swelling of calcium silicate hydrate (C-S-H) during water ingress. Analysing how the combination of deforming phase (C-S-H), non-deforming phase, and porosity affects the capillary water ingress rate is relevant for a deeper understanding of concrete durability. In this research, the C-S-H content was quantified by means of XRD diffraction coupled with Rietveld + PONKCS, dynamic water sorption (DVS), and SEM/BSE images coupled with phase mapping using PhAse Recognition and Characterization (PARC) software. The porosity was assessed by mercury intrusion porosimetry, water absorption under vacuum, and DVS. Furthermore, to assess deformations occurring with water and a non-aqueous imbibant, capillary imbibition tests with water and isopropanol as invading liquids were performed along with simultaneous deformation measurements. The relation between the relative C-S-H content and porosity has a great impact on the transport process. Samples exposed to isopropanol presented a much larger liquid uptake but significantly fewer deformations in comparison to imbibition with water. The effects of the changing pore structure were also evaluated with the Thomas and Jennings model, from which calculations indicated that pore shrink during imbibition. A comprehensive description of the relation between deformations and capillary imbibition in cement pastes reveals that liquid ingress is highly influenced by deformations.
Keyphrases
  • ionic liquid
  • machine learning
  • risk assessment
  • deep learning
  • convolutional neural network
  • density functional theory
  • high density