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A probabilistic-based methodology for predicting mould growth in façade constructions
- Publication Year :
- 2017
- Publisher :
- Elsevier, 2017.
-
Abstract
- Predicting mould growth on facade constructions during design is important for preventing financial loss, and ensuring a healthy and comfortable indoor environment. Uncertainties in predicting mould growth are related to the representation of the biological phenomenon, the climate exposure and the material uncertainties. This paper proposes a probabilistic-based methodology that assesses the performance of facade constructions against mould growth and accounts for the aforementioned uncertainties. A comprehensive representation of mould growth is ensured by integrating several mould models in a combined outcome. This approach enables a more comprehensible and useful illustration between continuous mould growth intensities and their corresponding likelihoods. The outdoor climate exposure is represented by stochastic models derived by real time-series analysis according to autoregressive–moving-average models. The methodology is applied to investigate the influence of several parameters and the performance of several construction assemblies. This paper proposes a method to evaluate the facade performance that can facilitate reliability-based design and optimisation of facade construction.
- Subjects :
- Probabilistic analysis
Engineering drawing
Engineering
Environmental Engineering
Stochastic modelling
020209 energy
Geography, Planning and Development
0211 other engineering and technologies
Timber
02 engineering and technology
Autoregressive-moving average model
021105 building & construction
0202 electrical engineering, electronic engineering, information engineering
Representation (mathematics)
Mould
Reliability (statistics)
Civil and Structural Engineering
Biological phenomenon
business.industry
Probabilistic logic
Uncertainty
Building and Construction
Industrial engineering
Facade
business
Sensitivity analysis
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....d4d9b5169b4b685a0bec4dc31c99186a