Influence of extruded mortar joints on mould growth in timber frame wall with brick veneer : Probabilistic and machine learning modelling
Bayat Pour, Mohsen LU ; Kahangi Shahreza, Seyedmohammad LU
and Abdul Hamid, Akram
LU
(2026)
In Journal of Building Physics
p.1-41
- Abstract
- Inappropriate bricklaying techniques can result in extruded mortar joints, which may partially fill the air gaps within brick veneer timber frame walls. This leads to an unintended connection or “bridge” between the brick masonry cladding and its adjacent layer (such as insulation or weather-resistant barriers). This bridging effect compromises the capillary break function of the air gap, allowing water to reach adjacent layers and intensifying the impact of water penetration, under wind-driven rain (WDR) conditions. This study employs a probabilistic analysis in combination with a machine learning metamodel to investigate the impact of extruded mortar joints on mould growth. The metamodel, developed using the random forests (RF) machine... (More)
- Inappropriate bricklaying techniques can result in extruded mortar joints, which may partially fill the air gaps within brick veneer timber frame walls. This leads to an unintended connection or “bridge” between the brick masonry cladding and its adjacent layer (such as insulation or weather-resistant barriers). This bridging effect compromises the capillary break function of the air gap, allowing water to reach adjacent layers and intensifying the impact of water penetration, under wind-driven rain (WDR) conditions. This study employs a probabilistic analysis in combination with a machine learning metamodel to investigate the impact of extruded mortar joints on mould growth. The metamodel, developed using the random forests (RF) machine learning algorithm, is used to predict the maximum mould index (MMI). In addition, this study assesses the effects of two different water penetration criteria—ASHRAE and experimental study-based (ES)—on extruded mortar joints under climatic conditions in Gothenburg, Sweden. In order to figure out how different orientations affect the analysis; the study examines the case study from four different orientations. The findings showed that the ES and ASHRAE criteria were in agreement for orientations with substantial WDR loads (e.g. south). On the other hand, the ASHRAE criteria illustrated a higher MMI than the ES criteria in walls facing orientations with relatively small WDR loads (e.g. north). Furthermore, an increased extruded mortar depth and higher MMI were shown to be positively correlated by the linear and non-linear mould sensitivity analyses. However, depending on the WDR loads (or different orientations) and chosen water penetration criteria, this correlation’s strength can vary. The air change rate (negative correlation), the solar absorption coefficient (negative correlation), the WDR’s reduction/splash coefficient (positive correlation), and the thermal conductivity of the Rockwool insulation (positive correlation) were additional important variables influencing the MMI. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/357d6978-021a-4f6d-8e73-2f40e5a9dc22
- author
- Bayat Pour, Mohsen
LU
; Kahangi Shahreza, Seyedmohammad
LU
and Abdul Hamid, Akram
LU
- organization
- publishing date
- 2026-02-02
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Mortar extrusion, Machine learning, Mould assessment, Clay brick masonry, Hygrothermal simulation, Probabilistic analysis, Water penetration
- in
- Journal of Building Physics
- pages
- 41 pages
- publisher
- SAGE Publications
- ISSN
- 1744-2583
- DOI
- 10.1177/17442591251414130
- language
- English
- LU publication?
- yes
- id
- 357d6978-021a-4f6d-8e73-2f40e5a9dc22
- date added to LUP
- 2026-02-02 14:58:40
- date last changed
- 2026-02-17 09:30:46
@article{357d6978-021a-4f6d-8e73-2f40e5a9dc22,
abstract = {{Inappropriate bricklaying techniques can result in extruded mortar joints, which may partially fill the air gaps within brick veneer timber frame walls. This leads to an unintended connection or “bridge” between the brick masonry cladding and its adjacent layer (such as insulation or weather-resistant barriers). This bridging effect compromises the capillary break function of the air gap, allowing water to reach adjacent layers and intensifying the impact of water penetration, under wind-driven rain (WDR) conditions. This study employs a probabilistic analysis in combination with a machine learning metamodel to investigate the impact of extruded mortar joints on mould growth. The metamodel, developed using the random forests (RF) machine learning algorithm, is used to predict the maximum mould index (MMI). In addition, this study assesses the effects of two different water penetration criteria—ASHRAE and experimental study-based (ES)—on extruded mortar joints under climatic conditions in Gothenburg, Sweden. In order to figure out how different orientations affect the analysis; the study examines the case study from four different orientations. The findings showed that the ES and ASHRAE criteria were in agreement for orientations with substantial WDR loads (e.g. south). On the other hand, the ASHRAE criteria illustrated a higher MMI than the ES criteria in walls facing orientations with relatively small WDR loads (e.g. north). Furthermore, an increased extruded mortar depth and higher MMI were shown to be positively correlated by the linear and non-linear mould sensitivity analyses. However, depending on the WDR loads (or different orientations) and chosen water penetration criteria, this correlation’s strength can vary. The air change rate (negative correlation), the solar absorption coefficient (negative correlation), the WDR’s reduction/splash coefficient (positive correlation), and the thermal conductivity of the Rockwool insulation (positive correlation) were additional important variables influencing the MMI.}},
author = {{Bayat Pour, Mohsen and Kahangi Shahreza, Seyedmohammad and Abdul Hamid, Akram}},
issn = {{1744-2583}},
keywords = {{Mortar extrusion; Machine learning; Mould assessment; Clay brick masonry; Hygrothermal simulation; Probabilistic analysis; Water penetration}},
language = {{eng}},
month = {{02}},
pages = {{1--41}},
publisher = {{SAGE Publications}},
series = {{Journal of Building Physics}},
title = {{Influence of extruded mortar joints on mould growth in timber frame wall with brick veneer : Probabilistic and machine learning modelling}},
url = {{http://dx.doi.org/10.1177/17442591251414130}},
doi = {{10.1177/17442591251414130}},
year = {{2026}},
}