Abstract

Tiles and slates are the two most commonly used roofing materials worldwide, yet many customers and construction professionals are frequently unsure about which product to choose. In this article, a network analysis based on a Bayesian machine learning approach was used to identify which are the main factors and how they influence architect decision about roofing tiles or slates. For that, the information collected from a survey conducted to 429 architects working in the Spanish construction sector was used to create a Bayesian network model that represents the conditional dependencies of the variables considered when the decision is taken. The results obtained allow to unveil aesthetic as the main factor conditioning architect decision for roofing tiles. In turn, the decision is more complex when roofing slates, since architects apart from the price they strongly considered the ability of installers in the decision process.

Highlights

  • An intense public debate always exists when it is required to decide whether use tiles or slates as roofing material

  • In order to carry out this analysis, this article proposes the development of a network analysis based on a Bayesian network structure that captures the probabilistic relationships between the variables involved in the decision making process

  • From the data collected through the questionnaires and interviews carried out to the 429 professionals who participated in the study, 175 missing values were detected during the data cleaning process

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Summary

Introduction

An intense public debate always exists when it is required to decide whether use tiles or slates as roofing material. Ceramic tiles (terracotta) replaced thatched roofs because of their i n su r m ou n t ab l e fire-resistance and longer lifespan [3]. At present, these properties still maintain tiles as a leading roofing material, existing varieties made from concrete or plastic. Roofing a building with ceramic tile costs under normal conditions around € 30 per square meter. Roof tiles are sensitive to temperature changes that may produce cracks where mold caused by humidity can reduce their lifespan

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