Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks
Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/8204568 |
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| _version_ | 1850218643943587840 |
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| author | Marijana Lazarevska Ana Trombeva Gavriloska Mirjana Laban Milos Knezevic Meri Cvetkovska |
| author_facet | Marijana Lazarevska Ana Trombeva Gavriloska Mirjana Laban Milos Knezevic Meri Cvetkovska |
| author_sort | Marijana Lazarevska |
| collection | DOAJ |
| description | Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns. |
| format | Article |
| id | doaj-art-3240a6eaac37409a83bc4db8da92ea8d |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3240a6eaac37409a83bc4db8da92ea8d2025-08-20T02:07:40ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/82045688204568Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural NetworksMarijana Lazarevska0Ana Trombeva Gavriloska1Mirjana Laban2Milos Knezevic3Meri Cvetkovska4Faculty of Civil Engineering, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaFaculty of Architecture, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Civil Engineering, University of Montenegro, 81000 Podgorica, MontenegroFaculty of Civil Engineering, University Ss Cyril and Methodius, 1000 Skopje, MacedoniaArtificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns.http://dx.doi.org/10.1155/2018/8204568 |
| spellingShingle | Marijana Lazarevska Ana Trombeva Gavriloska Mirjana Laban Milos Knezevic Meri Cvetkovska Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks Complexity |
| title | Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks |
| title_full | Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks |
| title_fullStr | Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks |
| title_full_unstemmed | Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks |
| title_short | Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks |
| title_sort | determination of fire resistance of eccentrically loaded reinforced concrete columns using fuzzy neural networks |
| url | http://dx.doi.org/10.1155/2018/8204568 |
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