Impact of extruded mortar joints on the hygrothermal performance of brick veneer walls : A probabilistic study
(2024) In Journal of Building Engineering 94.- Abstract
- Mortar extrusion during bricklaying, resulting from suboptimal workmanship, may act as a bridge linking clay brick masonry claddings and adjacent layers (e.g., insulation or weather-resistant barriers), thereby, facilitating water penetration into the adjacent layer owing to wind-driven rain (WDR). Despite the significant effect of water penetration on the hygrothermal performance of building envelopes, a consensus regarding the incorporation of WDR-induced water penetration into moisture safety designs and analyses remains lacking. In 2022, Kahangi Shahreza et al. tested two distinct brick types with three diverse mortar joint profiles. Consequently, they proposed a water penetration criterion that can be implemented in hygrothermal... (More)
- Mortar extrusion during bricklaying, resulting from suboptimal workmanship, may act as a bridge linking clay brick masonry claddings and adjacent layers (e.g., insulation or weather-resistant barriers), thereby, facilitating water penetration into the adjacent layer owing to wind-driven rain (WDR). Despite the significant effect of water penetration on the hygrothermal performance of building envelopes, a consensus regarding the incorporation of WDR-induced water penetration into moisture safety designs and analyses remains lacking. In 2022, Kahangi Shahreza et al. tested two distinct brick types with three diverse mortar joint profiles. Consequently, they proposed a water penetration criterion that can be implemented in hygrothermal analysis of clay brick masonry. The research in this paper combines the results of their study with probabilistic hygrothermal analysis by investigating the influence of extruded mortar on mould growth for a timber frame wall with brick masonry cladding. Two water penetration criteria, including ASHRAE and experimental study (ES) based on Kahangi Shahreza's study, were implemented while considering the different climatic conditions in Sweden. A metamodel, established using the random forests (RF) machine learning algorithm, serves as a tool for mould sensitivity analysis. The results of the probabilistic mould growth analysis revealed a congruence between the ASHRAE and ES criteria for locations with high WDR loads. However, in scenarios involving low WDR loads, the ASHRAE criterion yielded a higher maximum mould index than the ES criterion. In addition, the linear and non-linear mould sensitivity analyses demonstrated a positive correlation between the increase in the extruded mortar depth and elevated maximum mould indices. Nevertheless, the strength of this correlation is subject to alterations based on the WDR loads and selected water penetration criteria. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/57de0af6-86d6-4434-99bd-1e355f9c41ed
- author
- Bayat Pour, Mohsen LU and Kahangi, Mohammad LU
- organization
- publishing date
- 2024-06-13
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Mortar extrusion, Machine learning, Mould assessment, Clay brick masonry, Hygrothermal simulation
- in
- Journal of Building Engineering
- volume
- 94
- pages
- 21 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85196271250
- ISSN
- 2352-7102
- DOI
- 10.1016/j.jobe.2024.109936
- language
- English
- LU publication?
- yes
- id
- 57de0af6-86d6-4434-99bd-1e355f9c41ed
- date added to LUP
- 2024-06-23 17:51:24
- date last changed
- 2024-06-24 09:56:02
@article{57de0af6-86d6-4434-99bd-1e355f9c41ed,
abstract = {{Mortar extrusion during bricklaying, resulting from suboptimal workmanship, may act as a bridge linking clay brick masonry claddings and adjacent layers (e.g., insulation or weather-resistant barriers), thereby, facilitating water penetration into the adjacent layer owing to wind-driven rain (WDR). Despite the significant effect of water penetration on the hygrothermal performance of building envelopes, a consensus regarding the incorporation of WDR-induced water penetration into moisture safety designs and analyses remains lacking. In 2022, Kahangi Shahreza et al. tested two distinct brick types with three diverse mortar joint profiles. Consequently, they proposed a water penetration criterion that can be implemented in hygrothermal analysis of clay brick masonry. The research in this paper combines the results of their study with probabilistic hygrothermal analysis by investigating the influence of extruded mortar on mould growth for a timber frame wall with brick masonry cladding. Two water penetration criteria, including ASHRAE and experimental study (ES) based on Kahangi Shahreza's study, were implemented while considering the different climatic conditions in Sweden. A metamodel, established using the random forests (RF) machine learning algorithm, serves as a tool for mould sensitivity analysis. The results of the probabilistic mould growth analysis revealed a congruence between the ASHRAE and ES criteria for locations with high WDR loads. However, in scenarios involving low WDR loads, the ASHRAE criterion yielded a higher maximum mould index than the ES criterion. In addition, the linear and non-linear mould sensitivity analyses demonstrated a positive correlation between the increase in the extruded mortar depth and elevated maximum mould indices. Nevertheless, the strength of this correlation is subject to alterations based on the WDR loads and selected water penetration criteria.}},
author = {{Bayat Pour, Mohsen and Kahangi, Mohammad}},
issn = {{2352-7102}},
keywords = {{Mortar extrusion; Machine learning; Mould assessment; Clay brick masonry; Hygrothermal simulation}},
language = {{eng}},
month = {{06}},
publisher = {{Elsevier}},
series = {{Journal of Building Engineering}},
title = {{Impact of extruded mortar joints on the hygrothermal performance of brick veneer walls : A probabilistic study}},
url = {{http://dx.doi.org/10.1016/j.jobe.2024.109936}},
doi = {{10.1016/j.jobe.2024.109936}},
volume = {{94}},
year = {{2024}},
}