ARTIFICIAL NEURAL NETWORK MODEL FOR LOW STRENGTH RC BEAM SHEAR CAPACITY

Simple item page
dc.contributor.authorOwusu-Ansah, Emmanuel
dc.contributor.authorAfrifa, R. Owusu
dc.contributor.authorAdom-Asamoah, M.
dc.date.accessioned2024-12-04T11:00:34Z
dc.date.available2024-12-04T11:00:34Z
dc.date.issued2012-08
dc.descriptionThis article is published by Journal of Science and Technology 2012 and is also available at http://dx.doi.org/10.4314/just.v32i2.13
dc.description.abstractThis research was to investigate how the shear strength prediction of low strength reinforced concrete beams will improve under an ANN model. An existing database of 310 reinforced concrete beams without web reinforcement was divided into three sets of training, validation and testing. A total of 224 different architectural networks were tried, considering networks with one hidden layer as well as two hidden layers. Error measures of strength ratios were used to select the best ANN model which was then compared with 3 conventional design code equations in predicting the shear strength of 26 low strength RC beams. Even though the ANN was the most accurate, it was less conservative compared with the design code equations. A model reduction factor based on the characteristic strength concept is derived in this research and used to modify the ANN output. The modified ANN model is conservative in terms of safety and economy but not overly conservative as the conventional design equations. The procedure has been automated such that when new experimental sets are added to the database, the model can be updated and a new model could be developed.
dc.description.sponsorshipKNUST
dc.identifier.citationAfrifa et al.
dc.identifier.urihttp://dx.doi.org/10.4314/just.v32i2.13
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/16025
dc.language.isoen
dc.publisherJournal of Science and Technology
dc.titleARTIFICIAL NEURAL NETWORK MODEL FOR LOW STRENGTH RC BEAM SHEAR CAPACITY
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ajol-file-journals_229_articles_81396_submission_proof_81396-2725-193945-1-10-20120919.pdf
Size:
482.55 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Articles