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Application of sorptivity–diffusivity relationship for the refinement of hydraulic diffusivity function parameters obtained through inverse analysis

Application of sorptivity–diffusivity relationship for the refinement of hydraulic diffusivity... The modelling of water absorption in porous building materials is critical to the rational assessment of their performance during service. The non-linear diffusion equation has been extensively used to describe the phenomena with a two-parameter, moisture–dependent hydraulic diffusivity function, $$ D(\theta_{n} ) $$ D ( θ n ) , as the transport parameter. For a given material, the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) are often fitted through inverse modelling, which requires the non-destructive measurement of moisture profiles with expensive equipment. An alternative to this approach, is to use a sorptivity–diffusivity model which quantifies the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) on the basis of sorptivity determined through simple gravimetric measurements made in the course of a water-absorption test. However, the latter approach requires assuming the value of one of the two parameters to quantify the other. The objective of this paper is to demonstrate a novel combination of the two methods for obtaining refined estimates of parameters in $$ D(\theta_{n} ) $$ D ( θ n ) . The idea has been demonstrated by considering the instances of OPC–lime–sand mortar, fired clay brick, Lepine stone and sand specimen for which the moisture intrusion profiles and $$ D(\theta_{n} ) $$ D ( θ n ) modelled through inverse analysis have been reported previously by independent researchers. Analysis reveals that, the sorptivity–diffusivity model when used as a refinement tool for the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) fitted through inverse analysis leads to improvements of up to about 12% and 5% in the estimation of moisture content and penetration depth respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Building Pathology and Rehabilitation Springer Journals

Application of sorptivity–diffusivity relationship for the refinement of hydraulic diffusivity function parameters obtained through inverse analysis

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References (47)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer Nature Switzerland AG
Subject
Engineering; Building Repair and Maintenance; Structural Materials; Engineering Thermodynamics, Heat and Mass Transfer; Energy Efficiency; Building Materials
ISSN
2365-3159
eISSN
2365-3167
DOI
10.1007/s41024-019-0068-2
Publisher site
See Article on Publisher Site

Abstract

The modelling of water absorption in porous building materials is critical to the rational assessment of their performance during service. The non-linear diffusion equation has been extensively used to describe the phenomena with a two-parameter, moisture–dependent hydraulic diffusivity function, $$ D(\theta_{n} ) $$ D ( θ n ) , as the transport parameter. For a given material, the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) are often fitted through inverse modelling, which requires the non-destructive measurement of moisture profiles with expensive equipment. An alternative to this approach, is to use a sorptivity–diffusivity model which quantifies the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) on the basis of sorptivity determined through simple gravimetric measurements made in the course of a water-absorption test. However, the latter approach requires assuming the value of one of the two parameters to quantify the other. The objective of this paper is to demonstrate a novel combination of the two methods for obtaining refined estimates of parameters in $$ D(\theta_{n} ) $$ D ( θ n ) . The idea has been demonstrated by considering the instances of OPC–lime–sand mortar, fired clay brick, Lepine stone and sand specimen for which the moisture intrusion profiles and $$ D(\theta_{n} ) $$ D ( θ n ) modelled through inverse analysis have been reported previously by independent researchers. Analysis reveals that, the sorptivity–diffusivity model when used as a refinement tool for the parameters of $$ D(\theta_{n} ) $$ D ( θ n ) fitted through inverse analysis leads to improvements of up to about 12% and 5% in the estimation of moisture content and penetration depth respectively.

Journal

Journal of Building Pathology and Rehabilitation – Springer Journals

Published: Dec 1, 2020

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