A Novel Method for nZEB Internal Coverings Design Based on Neural Networks

Abstract

Research from the International Energy Agency about indoor ambiences and nearly zero energy buildings (nZEB) in the past has been centred on different aspects such as the prediction of indoor conditions as a function of the weather using laboratory material properties for simulations and real sampled data for validation. Thus, it is possible to use real data for defining behavioural groups of indoor ambiences as a function of real vapour permeability of internal coverings. However, this method is not suitable for modelling it and predicting its behaviour under weather changes, which is of interest to improve the method of selection and use of building construction materials. In this research, artificial intelligence procedures were employed as the first model of permeable coverings material behaviour to provide a newer understanding of building materials and applications for the generation of new control procedures between the mechanical and electronic point of view of building construction materials.

A Novel Method Based on Neural Networks for Designing Internal Coverings in Buildings: Energy Saving and Thermal Comfort

“…Thus, this methodology can be employed for almost all kinds of buildings and weather conditions [31,32]. Furthermore, this methodology would be of interest because climate variations will induce different indoor ambiences in each type of building and, therefore, this and others passive methods must be adjusted for each particular region [33][34][35][36]. Other kinds of artificial PCMs [37] are employed as building construction materials rather than the typical permeable internal covering materials, because these PCMs are well modelled in laboratories [37].…”

Towards High-Performance Buildings using IoT and AI technologies: A Comprehensive Review

“…The ANN is the paradigm that mimics human brain biological structure (Orosa et al 2019). It was developed in 1950 by McCulloch and Pitts (Wang 2019).…”

Applying Artificial Neural Networks In Construction

“…The use of neural networks in building materials to predict the characteristics of building materials, for example, for studying the effect of two types of materials including micro-silica and also calcium inosilicate minerals on the compressive strength of mortars [24], for estimating the volumetric water content in different times and positions during the water imbibition inside the porous building materials [25], for predicting performance of lightweight concrete with granulated expanded glass and ash aggregate [26], for designing the composition of cement stabilized rammed earth [27], for studying on adiabatic temperature rise reflecting hydration degree of concrete [28], for predicting the compressive strength of cement-based materials exposed to sulfate attack [29], for prediction of chloride diffusion in cement mortar [30] , etc. [31,32]. Neural networks are used to predict corrosion.…”