Predicting residual moment capacity of thermally insulated RC beams exposed to fire using artificial neural networks

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dc.contributor.authorErdem, Hakan
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2017
dc.departmentNiğde ÖHÜ
dc.description.abstractThis paper presents a method using artificial neural networks (ANNs) to predict the residual moment capacity of thermally insulated reinforced concrete (RC) beams exposed to fire. The use of heat resistant insulation material protects concrete beams against the harmful effects of fire. If it is desired to calculate the residual moment capacity of the beams in this state, the determination of the moment capacity of thermally insulated beams exposed to fire involves several consecutive calculations, which is significantly easier when ANNs are used. Beam width, beam effective depth, fire duration, concrete compressive and steel tensile strength, steel area, thermal conductivity of insulation material can influence behavior of RC beams exposed to high temperatures. In this study, a finite difference method was used to calculate the temperature distribution in a cross section of the beam, and temperature distribution, reduction mechanical properties of concrete and reinforcing steel and moment capacity were calculated using existing relations in literature. Data was generated for 336 beams with different beam width (by), beam account height (h), fire duration (t), mechanical properties of concrete (f(cd)) and reinforcing steel (f(yd)), steel area (A), insulation material thermal conductivity (kinsulation). Five input parameters (b(w), h, f(cd),f(yd) A(s) and k(insulation)) were used in the ANN to estimate the moment capacity (M-r). The trained model allowed the investigation of the effects on the moment capacity of the insulation material and the results indicated that the use of insulation materials with the smallest value of the thermal conductivities used in calculations is effective in protecting the RC beam against fire.
dc.identifier.doi10.12989/cac.2017.19.6.711
dc.identifier.endpage716
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85021155420
dc.identifier.scopusqualityQ1
dc.identifier.startpage711
dc.identifier.urihttps://dx.doi.org/10.12989/cac.2017.19.6.711
dc.identifier.urihttps://hdl.handle.net/11480/3492
dc.identifier.volume19
dc.identifier.wosWOS:000404351400012
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorErdem, Hakan
dc.language.isoen
dc.publisherTECHNO-PRESS
dc.relation.ispartofCOMPUTERS AND CONCRETE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectfire
dc.subjectthermally insulation material
dc.subjectthermal conductivity
dc.subjectresidual moment capacity
dc.subjectreinforced concrete
dc.subjectbeam
dc.subjectartificial neural networks
dc.titlePredicting residual moment capacity of thermally insulated RC beams exposed to fire using artificial neural networks
dc.typeArticle

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