Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer
Bars made of fiber reinforcement polymer (FRP) are in common usage for concrete reinforcing instead of steel reinforcing since steel could be affected by corrosion. The concrete beams reinforced by FRP bars have been studied mostly in longitudinal direction without shear reinforcement. The primary o...
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my.um.eprints.363512023-12-07T04:09:36Z http://eprints.um.edu.my/36351/ Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer Cao, Yan Fan, Qingming Azar, Sadaf Mahmoudi Alyousef, Rayed Yousif, Salim T. Wakil, Karzan Jermsittiparsert, Kittisak Ho, Lanh Si Alabduljabbar, Hisham Alaskar, Abdulaziz Bars made of fiber reinforcement polymer (FRP) are in common usage for concrete reinforcing instead of steel reinforcing since steel could be affected by corrosion. The concrete beams reinforced by FRP bars have been studied mostly in longitudinal direction without shear reinforcement. The primary objective of this investigation was to design and advance an algorithm for selection procedure of the parameters influence on prediction of shear resistance of reinforced concrete beams by FRP. Six input parameters were used which represent geometric and mechanical properties of the bars as well as shear features. These parameters are: web width, tensile re-inforcement depth, ratio of shear and depth, concrete compressive strength, ratio of FRP reinforcement, FRP modulus of elasticity and beam shear resistance. The searching algorithm is based on combination of artificial neural network and fuzzy logic principle or adaptive neuro fuzzy inference system (ANFIS). Based on the ob-tained results ratio of shear and depth has the strongest influence on the prediction of shear resistance of re-inforced concrete beams by FRP. Moreover, combination of tensile reinforcement depth and ratio of shear and depth is the most influential combination of two parameters on the prediction of shear resistance of reinforced concrete beams by FRP. Finally, combination of tensile reinforcement depth, ratio of shear and depth and FRP modulus of elasticity is the most influential combination of three parameters on the prediction of shear re-sistance of reinforced concrete beams by FRP. ELSEVIER SCIENCE INC 2020-10 Article NonPeerReviewed Cao, Yan and Fan, Qingming and Azar, Sadaf Mahmoudi and Alyousef, Rayed and Yousif, Salim T. and Wakil, Karzan and Jermsittiparsert, Kittisak and Ho, Lanh Si and Alabduljabbar, Hisham and Alaskar, Abdulaziz (2020) Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer. STRUCTURES, 27. pp. 118-127. |
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Bars made of fiber reinforcement polymer (FRP) are in common usage for concrete reinforcing instead of steel reinforcing since steel could be affected by corrosion. The concrete beams reinforced by FRP bars have been studied mostly in longitudinal direction without shear reinforcement. The primary objective of this investigation was to design and advance an algorithm for selection procedure of the parameters influence on prediction of shear resistance of reinforced concrete beams by FRP. Six input parameters were used which represent geometric and mechanical properties of the bars as well as shear features. These parameters are: web width, tensile re-inforcement depth, ratio of shear and depth, concrete compressive strength, ratio of FRP reinforcement, FRP modulus of elasticity and beam shear resistance. The searching algorithm is based on combination of artificial neural network and fuzzy logic principle or adaptive neuro fuzzy inference system (ANFIS). Based on the ob-tained results ratio of shear and depth has the strongest influence on the prediction of shear resistance of re-inforced concrete beams by FRP. Moreover, combination of tensile reinforcement depth and ratio of shear and depth is the most influential combination of two parameters on the prediction of shear resistance of reinforced concrete beams by FRP. Finally, combination of tensile reinforcement depth, ratio of shear and depth and FRP modulus of elasticity is the most influential combination of three parameters on the prediction of shear re-sistance of reinforced concrete beams by FRP. |
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Cao, Yan Fan, Qingming Azar, Sadaf Mahmoudi Alyousef, Rayed Yousif, Salim T. Wakil, Karzan Jermsittiparsert, Kittisak Ho, Lanh Si Alabduljabbar, Hisham Alaskar, Abdulaziz |
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Cao, Yan Fan, Qingming Azar, Sadaf Mahmoudi Alyousef, Rayed Yousif, Salim T. Wakil, Karzan Jermsittiparsert, Kittisak Ho, Lanh Si Alabduljabbar, Hisham Alaskar, Abdulaziz Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
author_facet |
Cao, Yan Fan, Qingming Azar, Sadaf Mahmoudi Alyousef, Rayed Yousif, Salim T. Wakil, Karzan Jermsittiparsert, Kittisak Ho, Lanh Si Alabduljabbar, Hisham Alaskar, Abdulaziz |
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Cao, Yan |
title |
Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
title_short |
Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
title_full |
Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
title_fullStr |
Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
title_full_unstemmed |
Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
title_sort |
computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer |
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ELSEVIER SCIENCE INC |
publishDate |
2020 |
url |
http://eprints.um.edu.my/36351/ |
_version_ |
1787132381913677824 |
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13.18916 |