Properties and material models for common construction materials at elevated temperatures
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- @Article{NASER:2019:CBM,
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author = "M. Z. Naser",
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title = "Properties and material models for common construction
materials at elevated temperatures",
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journal = "Construction and Building Materials",
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volume = "215",
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pages = "192--206",
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year = "2019",
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ISSN = "0950-0618",
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DOI = "doi:10.1016/j.conbuildmat.2019.04.182",
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URL = "http://www.sciencedirect.com/science/article/pii/S0950061819310712",
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keywords = "genetic algorithms, genetic programming, Material
models, Construction materials, Fire resistance,
Artificial intelligence",
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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
Citations