Strength prediction of rotary brace damper using MLR and MARS
This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled wit...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Techno Press
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/71916/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991435097&doi=10.12989%2fsem.2016.60.3.471&partnerID=40&md5=610b6c7e9ae7ec0a23009ce838af5b20 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181. |
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