A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm
The most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral res...
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Wiley
2020-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/3684678 |
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| author | Seyed Abdonnabi Razavi Navid Siahpolo Mehdi Mahdavi Adeli |
| author_facet | Seyed Abdonnabi Razavi Navid Siahpolo Mehdi Mahdavi Adeli |
| author_sort | Seyed Abdonnabi Razavi |
| collection | DOAJ |
| description | The most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new and accurate correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using the genetic algorithm (GA). For this purpose, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3−, 6−, 9−, 12−, 15, and 20− story steel EBF frames were considered with 3 different types of link beam, 3 different types of column stiffness, and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models were analyzed to reach 4 different performance levels. 6769 data were used as GA training data. Moreover, to validate the correlation, 2257 data were used as test data for calculating mean squared error (MSE) and correlation coefficient (R) between the predicted values of (q) and the real values. In addition, the MSE and R were calculated for correlation in the train and test data. Also, the comparison of the response of maximum inelastic displacement of 5 stories EBF from the proposed correlation and the mean inelastic time-history analysis confirms the accuracy of the estimate relationship. |
| format | Article |
| id | doaj-art-2892e16bd79a4f7fab595f293802c94f |
| institution | Kabale University |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
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| series | Journal of Engineering |
| spelling | doaj-art-2892e16bd79a4f7fab595f293802c94f2025-02-03T05:51:15ZengWileyJournal of Engineering2314-49042314-49122020-01-01202010.1155/2020/36846783684678A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic AlgorithmSeyed Abdonnabi Razavi0Navid Siahpolo1Mehdi Mahdavi Adeli2Department of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, IranDepartment of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, IranDepartment of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, IranThe most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new and accurate correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using the genetic algorithm (GA). For this purpose, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3−, 6−, 9−, 12−, 15, and 20− story steel EBF frames were considered with 3 different types of link beam, 3 different types of column stiffness, and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models were analyzed to reach 4 different performance levels. 6769 data were used as GA training data. Moreover, to validate the correlation, 2257 data were used as test data for calculating mean squared error (MSE) and correlation coefficient (R) between the predicted values of (q) and the real values. In addition, the MSE and R were calculated for correlation in the train and test data. Also, the comparison of the response of maximum inelastic displacement of 5 stories EBF from the proposed correlation and the mean inelastic time-history analysis confirms the accuracy of the estimate relationship.http://dx.doi.org/10.1155/2020/3684678 |
| spellingShingle | Seyed Abdonnabi Razavi Navid Siahpolo Mehdi Mahdavi Adeli A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm Journal of Engineering |
| title | A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm |
| title_full | A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm |
| title_fullStr | A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm |
| title_full_unstemmed | A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm |
| title_short | A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm |
| title_sort | new empirical correlation for estimation of ebf steel frame behavior factor under near fault earthquakes using the genetic algorithm |
| url | http://dx.doi.org/10.1155/2020/3684678 |
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