Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1523
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yavuz, Günnur | - |
dc.date.accessioned | 2021-12-13T10:41:28Z | - |
dc.date.available | 2021-12-13T10:41:28Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 1598-8198 | - |
dc.identifier.issn | 1598-818X | - |
dc.identifier.uri | https://doi.org/10.12989/cac.2019.23.1.049 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1523 | - |
dc.description.abstract | In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum R-2 approximate to 0.97). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters. | en_US |
dc.language.iso | en | en_US |
dc.publisher | TECHNO-PRESS | en_US |
dc.relation.ispartof | COMPUTERS AND CONCRETE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Internal Frp Bar | en_US |
dc.subject | Reinforced Concrete | en_US |
dc.subject | Beam | en_US |
dc.subject | Shear Strength | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Reinforced Concrete Beams | en_US |
dc.subject | Prediction | en_US |
dc.subject | Capacity | en_US |
dc.subject | Polymer | en_US |
dc.title | Determining the shear strength of FRP-RC beams using soft computing and code methods | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.12989/cac.2019.23.1.049 | - |
dc.identifier.scopus | 2-s2.0-85061162834 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümü | en_US |
dc.identifier.volume | 23 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 49 | en_US |
dc.identifier.endpage | 60 | en_US |
dc.identifier.wos | WOS:000457493400005 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 6603867460 | - |
dc.identifier.scopusquality | Q2 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | 02.02. Department of Civil Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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