Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/33043
DC Field | Value | Language |
---|---|---|
dc.contributor.author | AlHamaydeh, Mohammad | - |
dc.contributor.author | Markou, George | - |
dc.contributor.author | Bakas, Nikolas | - |
dc.contributor.author | Papadrakakis, Manolis | - |
dc.date.accessioned | 2024-10-09T05:56:23Z | - |
dc.date.available | 2024-10-09T05:56:23Z | - |
dc.date.issued | 2022-08-01 | - |
dc.identifier.citation | Engineering Structures, 2022, vol.264 | en_US |
dc.identifier.issn | 01410296 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/33043 | - |
dc.description.abstract | The presented work utilizes Artificial Intelligence (AI) algorithms, to model and interpret the behavior of the fiber reinforced polymer (FRP)-reinforced concrete deep beams without stirrups. This is done by first running an extensive nonlinear finite element analysis (NLFEA) investigation, spanning across the practical ranges of the different input parameters. The FEA modeling is meticulously validated against published experimental results. A total of 93 different models representing a multitude of possible FRP-reinforced deep beam designs are rigorously analyzed. The results are then utilized in building an AI-model that describes the shear capacity for FRP-reinforced deep beams. The study investigates the effect of several factors on the shear capacity and identifies the vital parameters to be used for further model development. Additionally, the developed AI-model is benchmarked against several design standards for blind predictions on new unseen data and design codes, namely: the EC, ACI 440.1R-15, and the modified ACI 440.1R-15 (for size effect). The AI-model demonstrated superior generalization on the blind prediction dataset in comparison to the design codes. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Engineering Structures | en_US |
dc.subject | Nonlinear FEA | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | FRP | en_US |
dc.subject | Deep Beams without Stirrups | en_US |
dc.title | AI-based shear capacity of FRP-reinforced concrete deep beams without stirrups | en_US |
dc.type | Article | en_US |
dc.collaboration | University of Pretoria | en_US |
dc.collaboration | American University of Sharjah | en_US |
dc.collaboration | The Cyprus Institute | en_US |
dc.collaboration | National Technical University Of Athens | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.subject.category | ENGINEERING AND TECHNOLOGY | en_US |
dc.subject.category | Civil Engineering | en_US |
dc.journals | Subscription | en_US |
dc.country | Greece | en_US |
dc.country | South Africa | en_US |
dc.country | United Arab Emirates | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.engstruct.2022.114441 | en_US |
dc.identifier.scopus | 2-s2.0-85131041957 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85131041957 | - |
dc.relation.volume | 264 | en_US |
cut.common.academicyear | empty | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Civil Engineering and Geomatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-5004-0778 | - |
crisitem.author.orcid | 0000-0002-6891-7064 | - |
crisitem.author.orcid | 0000-0002-1890-8792 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
crisitem.journal.journalissn | 0141-0296 | - |
crisitem.journal.publisher | Elsevier | - |
Appears in Collections: | Άρθρα/Articles |
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