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• W4238968421 abstract "The presence of moisture in building envelopes caused by infiltration or condensation, especially in insulation layers, can have serious consequences in the whole-building energy performance and thermal comfort. Accurate prediction of moisture transport in buildings depends on properly understanding how water migrates across an interface, and it is usually performed by associating experimental analysis of different types of porous media or by numerical simulation. With the objective of reducing the energy consumption of buildings, computational tools are being used to simulate new and retrofitting buildings. In this type of application, it is common to find nonlinear behavior affecting temperature and relative humidity profiles in building structures, mainly due to modeling difficulty and highly moisture-dependent properties, increasing the difference between the results found by computational simulations and what happens inside building materials. Based on these concepts, this chapter presents two black-box approaches, with nonlinear identification focus, adopting Multivariate Adaptive Regression Splines (MARS) and Least Squares Support Vector Machines (LS-SVM). The first technique was considered to reproduce the behavior of highly hygroscopic building materials. Considering an experimental data set acquired using an experimental plant developed to study moisture effects on building surfaces. MARS models were built in order to predict heat flux, mass flow rates, and both temperature and relative humidity profiles considering just indoor and outdoor surface temperature and relative humidity as inputs. In the second approach, by adopting multiples MISO (multiple-input, single-output) Nonlinear Auto-Regressive with eXogenous inputs (NARX) models, LS-SVM, a maximum margin model based on structural risk minimization, was used to predict vapor flux, sensible heat flux, latent heat flux and mould growth risk in roofs surfaces. In this second case study, outdoor weather conditions were considered as input for the models. To evaluate the proposed black-box regression and identification techniques, five performance coefficients were analyzed for both training and validation phases. Results of applying artificial intelligence based approaches in predicting the hygrothermal behavior of building materials showed consistent precision when compared to the results of both experimental and numerical model results." @default.
• W4238968421 created "2022-05-12" @default.
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• W4238968421 date "2021-07-05" @default.
• W4238968421 modified "2023-10-17" @default.
• W4238968421 title "Distinct approaches to reproduce hygrothermal behavior of building materials based black-box models" @default.
• W4238968421 doi "https://doi.org/10.1515/9783110584455-005" @default.
• W4238968421 hasPublicationYear "2021" @default.
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