کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6698281 | 1428364 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A probabilistic-based methodology for predicting mould growth in façade constructions
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A probabilistic-based methodology for predicting mould growth in façade constructions A probabilistic-based methodology for predicting mould growth in façade constructions](/preview/png/6698281.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Building and Environment - Volume 128, 15 January 2018, Pages 33-45
Journal: Building and Environment - Volume 128, 15 January 2018, Pages 33-45
نویسندگان
Klodian Gradeci, Nathalie Labonnote, Berit Time, Jochen Köhler,