A probabilistic-based methodology for predicting mould growth in façade constructions. (15th January 2018)

• Record Type: Journal Article
• Authors: Gradeci, Klodian; Labonnote, Nathalie; Time, Berit; Köhler, Jochen
• Is Part Of: Building and environment. Volume 128(2018)
• Page Start: 33
• Page End: 45
• Publication Date: 2018-01-15
• Subjects: Mould -- Probabilistic analysis -- Autoregressive-moving average model -- Sensitivity analysis -- Uncertainty -- Timber; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2017.11.021
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Predicting mould growth on façade 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 façade 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 façade performance that can facilitate reliability-based design and optimisation of façade construction. Highlights: A probabilistic-based methodology for predicting mould growth is developed. A comprehensive representation of mould growth and its assessment is proposed. The stochastic representation of the climate exposure is accounted for. Sensitivity of different parameters affecting the outcome are investigated.