Properties and material models for common construction materials at elevated temperatures

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@Article{NASER:2019:CBM,

• author = "M. Z. Naser",
• title = "Properties and material models for common construction materials at elevated temperatures",
• journal = "Construction and Building Materials",
• volume = "215",
• pages = "192--206",
• year = "2019",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2019.04.182",
• URL = "http://www.sciencedirect.com/science/article/pii/S0950061819310712",
• keywords = "genetic algorithms, genetic programming, Material models, Construction materials, Fire resistance, Artificial intelligence",
• abstract = "Construction building materials experience physio-chemical and phase changes when subjected to elevated temperatures. These changes are often defined through temperature-dependent material models. A cross examination of adopted models reveals that such models markedly varies across open literature and fire guides (i.e. ASCE, Eurocodes etc.). This, not only complicates the process of fire analysis and design, but can also hinders ongoing standardization initiatives. In support of these initiatives, this paper leverages symbolic regression through artificial neural networks (ANN) and genetic programming (GP) to arrive at representative temperature-dependent thermal and mechanical material models for common building materials, namely: normal strength concrete, masonry, structural steel, stainless steel, cold-formed steel and wood. The proposed material models have the potential to regulate and modernize structural design under extreme loading conditions, i.e. fire. The result of this investigation demonstrates the value of using artificial intelligence (AI) into comprehending the complex nature of temperature-induced effects on building materials; together with deriving associated temperature-dependent models",

}

Genetic Programming entries for M Z Naser

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