Properties and material models for modern construction materials at elevated temperatures

Abstract

The construction industry has adopted a number of new building materials over the past few years. While these materials are specifically designed to achieve improved strength and durability characteristics at ambient conditions, the performance of modern construction materials (MCMs) under extreme conditions such as fire is still not understood. Under elevated temperatures, MCMs not only undergo a series of physio-chemical degradations, but these degradations are often of a much severe magnitude than that in traditional construction materials (TCMs). Despite ongoing efforts, there continues to be a lack of guidance/provisions on how to account for such temperature-induced degradations in MCMs. This adds another dimension of complexity to researchers and engineers seeking to carry out fire resistance evaluation and also presents a major challenge towards promoting standardization and performance-based solutions for fire engineering applications. In order to bridge this knowledge gap, this paper presents a methodology to develop temperature-dependent material models for MCMs such as high strength/performance concrete (HSC/HPC), high/very high strength steels (HSS/VHSS), and fiber-reinforced polymer (FRP) composites, using two techniques of artificial intelligence (AI) namely: artificial neural networks (ANNs) and evolutionary genetic algorithms (GAs). The outcome of this study showcases the merit of integrating AI into understanding the complex behavior of MCMs under fire conditions as well as in deriving temperature-dependent material models for these materials.