Techno Press
Techno Press

Structural Engineering and Mechanics   Volume 60, Number 3, November10 2016, pages 471-488
DOI: http://dx.doi.org/10.12989/sem.2016.60.3.471
 
Strength prediction of rotary brace damper using MLR and MARS
I. Mansouri, M. Safa, Z. Ibrahim, O. Kisi, M.M. Tahir, S. Baharom and M. Azimi

 
Abstract     [Full Text]
    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.
 
Key Words
    rotary brace damper; passive energy dissipation; nonlinear response; MLR; MARS; damper strength
 
Address
I. Mansouri: Deptartment of Civil Engineering, Birjand University of Technology, Birjand, Iran
M. Safa, Z. Ibrahim: Deptartment of Civil Engineering, University of Malaya, Kuala Lumpur, Malaysia
O. Kisi: Center for Interdisciplinary Research, International Black Sea University, Tbilisi, Georgia
M.M. Tahir: UTM CRC, Institute for Smart Infrastructure and Innovative Construction, UTM, Johor Bahru, Malaysia
S. Baharom: Department of Civil and Structural Engineering, National University of Malaysia, Malaysia
M. Azimi: Department of Quantity Surveying, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
 

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