An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer

The use of externally bonded and near-surface mounted reinforcement of CFRP on flexure beams with the use of epoxy or resin is an effective strengthening and repairing technique for RC beams. For our study 158 beams specimens were gathered for the development of flexural strength analysis through th...

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Main Authors: Arcilla, Chelsea Marguerite V., Vilaga, Frances Ira B.
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Language:English
Published: Animo Repository 2001
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/5156
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-57272021-04-12T01:16:12Z An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer Arcilla, Chelsea Marguerite V. Vilaga, Frances Ira B. The use of externally bonded and near-surface mounted reinforcement of CFRP on flexure beams with the use of epoxy or resin is an effective strengthening and repairing technique for RC beams. For our study 158 beams specimens were gathered for the development of flexural strength analysis through the use of artificial neural network modeling. The 158 beam specimens are divided into three groups 70% of the data were used for the training, 15% were tested, and the remaining 15% were used for verification. The input parameters used are the easily available variables within the journals, namely: base (b), height (h), length (L), cross-sectional area of the tension enforcement bar (As), cross-sectional area of the CFRP plate/sheet (Af), and the yield strength of CFRP (ftf) the output parameter is the ultimate flexure moment. The developed ANN model is then compared with other existing models to determine its efficiency and accuracy. Lastly, a parametric study was done in order to determine the importance, effect, and behavior of each parameter. Through this, the proponents of this study were able to produce a reliable flexural behavior model of a RC beam strengthened with CFRP even more efficient and effective than that of other studies of the same topic. 2001-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/5156 Bachelor's Theses English Animo Repository Concrete beams Flexure--Testing Strength of materials--Testing Civil Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Concrete beams
Flexure--Testing
Strength of materials--Testing
Civil Engineering
spellingShingle Concrete beams
Flexure--Testing
Strength of materials--Testing
Civil Engineering
Arcilla, Chelsea Marguerite V.
Vilaga, Frances Ira B.
An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
description The use of externally bonded and near-surface mounted reinforcement of CFRP on flexure beams with the use of epoxy or resin is an effective strengthening and repairing technique for RC beams. For our study 158 beams specimens were gathered for the development of flexural strength analysis through the use of artificial neural network modeling. The 158 beam specimens are divided into three groups 70% of the data were used for the training, 15% were tested, and the remaining 15% were used for verification. The input parameters used are the easily available variables within the journals, namely: base (b), height (h), length (L), cross-sectional area of the tension enforcement bar (As), cross-sectional area of the CFRP plate/sheet (Af), and the yield strength of CFRP (ftf) the output parameter is the ultimate flexure moment. The developed ANN model is then compared with other existing models to determine its efficiency and accuracy. Lastly, a parametric study was done in order to determine the importance, effect, and behavior of each parameter. Through this, the proponents of this study were able to produce a reliable flexural behavior model of a RC beam strengthened with CFRP even more efficient and effective than that of other studies of the same topic.
format text
author Arcilla, Chelsea Marguerite V.
Vilaga, Frances Ira B.
author_facet Arcilla, Chelsea Marguerite V.
Vilaga, Frances Ira B.
author_sort Arcilla, Chelsea Marguerite V.
title An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
title_short An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
title_full An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
title_fullStr An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
title_full_unstemmed An artificial neural network modeling of the flexural behavior of an RC beam strengthened using carbon fiber-reinforced polymer
title_sort artificial neural network modeling of the flexural behavior of an rc beam strengthened using carbon fiber-reinforced polymer
publisher Animo Repository
publishDate 2001
url https://animorepository.dlsu.edu.ph/etd_bachelors/5156
_version_ 1712576317501734912