Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines

Abstract

Abstract:The hygrothermal analysis of roofs is relevant due to the large areas exposed to a wide range of weather conditions, these directly affecting the energy performance and thermal comfort of buildings. However, after a long life service, the solar absorptivity coatings of roofs can be altered by mould accumulation. Based on two well established mathematical models, one that adopts driving potentials to calculate temperature, moist air pressure and water vapor pressure gradients, and the other to estimate the mould growth risk on surfaces, this research introduces an approach to predict mould growth considering a reduced computational effort and simulation time. By adopting multiple MISO (Multiple-Input, Single-Output) Nonlinear AutoRegressive with eXogenous inputs (NARX) models, a machine learning technique known as Least Squares Support Vector Machines (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 on roof surfaces. The proposed model was validated in terms of the Multiple Correlation Coefficient (R 2 ), Mean Square Error (MSE) and Mean Absolute Error (MAE) performance indices considering as input the weather file from Curitiba city-Brazil, showing consistent precision when compared to the results of a validated numerical model.

Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

“…Based on recent studies focusing on time series forecasting [45][46][47][48], the coefficient of determination R 2 was assumed as a performance criterion for model evaluation; see Equation (18). Thus, γ i is the mean of the targets (γ i ), and these values represent the observed data-those acquired using the ultrasound equipment.…”

Hygrothermal Modelling of the Differences between Single versus Variable Relative Humidity Vapour Diffusion Resistivity Properties of Pliable Membranes

“…The integration of hygrothermal simulation tools has facilitated several investigations into the performance of wall systems as a whole, and component analysis of individual elements within the building envelope [14][15][16][17][18]. While some studies have also looked into hygrothermal performance of whole building at larger scale, with specific attention to preserving heritage and historic buildings and improving their energy efficiency [19][20][21][22][23], some studies have explored risks associated with emerging materials, such as Cross Laminated Timber buildings [24][25][26][27][28]. However, more recent research has been undertaking more specific empirical validation tasks and questioning the material properties and calculation methods within the hygrothermal simulation tools.…”

Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

“…In recent years, a machine learning algorithm, the Support Vector Machine (SVM), has been used in various research fields for classification and time series forecasting. Freire et al [8] assessed the potential of Support Vector Regression (SVR) for short-term prediction of mold growth, vapor flux, and sensible and latent heat fluxes on roof surfaces. Eighteen months of climate data for the city of Curitiba in Brazil were evenly divided for training and testing purposes.…”