FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367610375> ?p ?o ?g. }

• W4367610375 endingPage "594" @default.
• W4367610375 startingPage "581" @default.
• W4367610375 abstract "Reinforced concrete (RC) slabs are integral parts of building structures and provide compartmentation functionality when subjected to fire. However, the fire resistance of RC slabs is affected by numerous factors that are mostly inherent uncertainties. Therefore, uncertainty and sensitivity analyses were conducted to examine the influence and significance of the potential uncertain parameters on the fire resistance of RC slabs. To this end, a set of RC slab samples with various design parameters were generated and different fire scenarios were considered using parametric fire curves. The slab samples were randomly coupled with the fire scenario samples. For each of the slab-fire pairs, finite-element simulations were conducted, and three specific fire durations were identified to represent the slab fire resistance corresponding to the failure criteria on the steel temperature (SP-Ⅰ), unexposed surface temperature (SP-Ⅱ), and mid-span deflection (SP- III). As a consequence, a database was established by collecting the calculated fire resistance for all the considered slab-fire samples. The prediction models of the slab fire resistance in association with uncertain parameters were developed by four commonly used ML algorithms, including linear regression, random forest, gradient boosting decision tree, and extreme gradient boosting. Among the developed ML-based prediction models, the extreme gradient boosting model was proven to have superior predictive accuracy. Therefore, it was further utilized for uncertainty analysis by considering 14 uncertain parameters from fire scenarios, material strengths, geometric dimensions, and external loads. The uncertainty analysis results showed that the considered uncertain parameters cause significant variability in the fire resistance of slabs, and the coefficients of variation were 11.5%, 13.1%, and 20.6% for the fire durations related to SP-Ⅰ, SP-Ⅱ, and SP- III, respectively. Moreover, the SHapley Additive exPlanations method was used to examine the sensitivity of the considered uncertain parameters. It was found that the parameters related to fire scenario were more influential to the fire resistance of slabs than the slab design parameters. The opening factor, slab thickness, and fire load density had noticeable effects on the fire resistance of SP-Ⅰ, SP-Ⅱ, and SP- III, respectively, whereas the uncertainties from material strengths and convection conditions had negligible effects." @default.
• W4367610375 created "2023-05-02" @default.
• W4367610375 creator A5029832401 @default.
• W4367610375 creator A5032085236 @default.
• W4367610375 creator A5037564577 @default.
• W4367610375 creator A5051555351 @default.
• W4367610375 date "2023-07-01" @default.
• W4367610375 modified "2023-10-15" @default.
• W4367610375 title "Machine-Learning-Based uncertainty and sensitivity analysis of Reinforced-Concrete slabs subjected to fire" @default.
• W4367610375 cites W1550944210 @default.
• W4367610375 cites W1973595648 @default.
• W4367610375 cites W1988790447 @default.
• W4367610375 cites W1990271966 @default.
• W4367610375 cites W1991283397 @default.
• W4367610375 cites W2006549386 @default.
• W4367610375 cites W2033137898 @default.
• W4367610375 cites W2055339266 @default.
• W4367610375 cites W2074375619 @default.
• W4367610375 cites W2081645295 @default.
• W4367610375 cites W2129489531 @default.
• W4367610375 cites W2162341947 @default.
• W4367610375 cites W2166318314 @default.
• W4367610375 cites W2170393021 @default.
• W4367610375 cites W2797556496 @default.
• W4367610375 cites W2885501072 @default.
• W4367610375 cites W2904488952 @default.
• W4367610375 cites W2907292342 @default.
• W4367610375 cites W2911964244 @default.
• W4367610375 cites W2978151602 @default.
• W4367610375 cites W3010453543 @default.
• W4367610375 cites W3011386291 @default.
• W4367610375 cites W3014945915 @default.
• W4367610375 cites W3045019663 @default.
• W4367610375 cites W3096035337 @default.
• W4367610375 cites W3102476541 @default.
• W4367610375 cites W3132596425 @default.
• W4367610375 cites W3134790670 @default.
• W4367610375 cites W3137964569 @default.
• W4367610375 cites W3154411971 @default.
• W4367610375 cites W3154512804 @default.
• W4367610375 cites W3191764388 @default.
• W4367610375 cites W3192428951 @default.
• W4367610375 cites W3193914348 @default.
• W4367610375 cites W3216785159 @default.
• W4367610375 cites W4205413892 @default.
• W4367610375 cites W4205803210 @default.
• W4367610375 cites W4288460543 @default.
• W4367610375 cites W4320406333 @default.
• W4367610375 doi "https://doi.org/10.1016/j.istruc.2023.04.030" @default.
• W4367610375 hasPublicationYear "2023" @default.
• W4367610375 type Work @default.
• W4367610375 citedByCount "0" @default.
• W4367610375 crossrefType "journal-article" @default.
• W4367610375 hasAuthorship W4367610375A5029832401 @default.
• W4367610375 hasAuthorship W4367610375A5032085236 @default.
• W4367610375 hasAuthorship W4367610375A5037564577 @default.
• W4367610375 hasAuthorship W4367610375A5051555351 @default.
• W4367610375 hasConcept C105795698 @default.
• W4367610375 hasConcept C113740112 @default.
• W4367610375 hasConcept C117251300 @default.
• W4367610375 hasConcept C119857082 @default.
• W4367610375 hasConcept C120665830 @default.
• W4367610375 hasConcept C121332964 @default.
• W4367610375 hasConcept C127413603 @default.
• W4367610375 hasConcept C135628077 @default.
• W4367610375 hasConcept C159985019 @default.
• W4367610375 hasConcept C169258074 @default.
• W4367610375 hasConcept C192562407 @default.
• W4367610375 hasConcept C21200559 @default.
• W4367610375 hasConcept C24326235 @default.
• W4367610375 hasConcept C2781355719 @default.
• W4367610375 hasConcept C2987912017 @default.
• W4367610375 hasConcept C33923547 @default.
• W4367610375 hasConcept C39432304 @default.
• W4367610375 hasConcept C41008148 @default.
• W4367610375 hasConcept C66938386 @default.
• W4367610375 hasConcept C70153297 @default.
• W4367610375 hasConceptScore W4367610375C105795698 @default.
• W4367610375 hasConceptScore W4367610375C113740112 @default.
• W4367610375 hasConceptScore W4367610375C117251300 @default.
• W4367610375 hasConceptScore W4367610375C119857082 @default.
• W4367610375 hasConceptScore W4367610375C120665830 @default.
• W4367610375 hasConceptScore W4367610375C121332964 @default.
• W4367610375 hasConceptScore W4367610375C127413603 @default.
• W4367610375 hasConceptScore W4367610375C135628077 @default.
• W4367610375 hasConceptScore W4367610375C159985019 @default.
• W4367610375 hasConceptScore W4367610375C169258074 @default.
• W4367610375 hasConceptScore W4367610375C192562407 @default.
• W4367610375 hasConceptScore W4367610375C21200559 @default.
• W4367610375 hasConceptScore W4367610375C24326235 @default.
• W4367610375 hasConceptScore W4367610375C2781355719 @default.
• W4367610375 hasConceptScore W4367610375C2987912017 @default.
• W4367610375 hasConceptScore W4367610375C33923547 @default.
• W4367610375 hasConceptScore W4367610375C39432304 @default.
• W4367610375 hasConceptScore W4367610375C41008148 @default.
• W4367610375 hasConceptScore W4367610375C66938386 @default.
• W4367610375 hasConceptScore W4367610375C70153297 @default.
• W4367610375 hasFunder F4320321001 @default.

• next