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 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.

[1]  Saiful Amri Mazlan,et al.  A design and modelling review of rotary magnetorheological damper , 2013 .

[2]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines for analysis of geotechnical engineering systems , 2013 .

[3]  Thomas Schumacher,et al.  Eulerian-based virtual visual sensors to detect natural frequencies of structures , 2015 .

[4]  Meldi Suhatril,et al.  A new finite element investigation on pre-bent steel strips as damper for vibration control , 2011 .

[5]  P. S. Els,et al.  Semi-active rotary damper for a heavy off-road wheeled vehicle , 1999 .

[6]  Mohammad Ismail,et al.  Kenaf Fiber Reinforced Polymer Composites for Strengthening RC Beams , 2014 .

[7]  Khaled Alenezi,et al.  Behavior of pre-cast U-Shaped Composite Beam integrating cold-formed steel with ferro-cement slab , 2016 .

[8]  Meldi Suhatril,et al.  Behaviour of C-shaped angle shear connectors under monotonic and fully reversed cyclic loading: An experimental study , 2012 .

[9]  Mahdi Shariati,et al.  Behavior of Tilted Angle Shear Connectors , 2015, PloS one.

[10]  Meldi Suhatril,et al.  Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios , 2013 .

[11]  Thomas Schumacher,et al.  Monitoring of Structures and Mechanical Systems Using Virtual Visual Sensors for Video Analysis: Fundamental Concept and Proof of Feasibility , 2013, Sensors.

[12]  Mahdi Shariati,et al.  Shear Capacity of C-Shaped and L-Shaped Angle Shear Connectors , 2016, PloS one.

[13]  Mark A. Bradford,et al.  Experimental study of flush end plate beam-to-CFST column composite joints with deconstructable bolted shear connectors , 2015 .

[14]  Hiroshi Tagawa,et al.  Seismic response of steel structures with seesaw systems using viscoelastic dampers , 2013 .

[15]  Saiful Amri Mazlan,et al.  Design of magnetorheological damper with a combination of shear and squeeze modes , 2014 .

[16]  Pijush Samui,et al.  Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles , 2012, Neural Computing and Applications.

[17]  J. de Brito,et al.  Statistical modelling of carbonation in reinforced concrete , 2014 .

[18]  Andrew S. Whittaker,et al.  Energy dissipation systems for seismic applications: Current practice and recent developments , 2008 .

[19]  H. Nezamabadi-pour,et al.  Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams , 2013 .

[20]  Ronald McCaffer,et al.  Tests and methods of evaluating the self-healing efficiency of concrete: A review , 2016 .

[21]  M. R. Bhutta,et al.  Influence of non-hydrocarbon substances on the compressive strength of natural rubber latex-modified concrete , 2012 .

[22]  Mahmood Md. Tahir,et al.  Seismic performance of ductility classes medium RC beam-column connections with continuous rectangular spiral transverse reinforcements , 2015 .

[23]  Q. Huang,et al.  An efficient algorithm for simultaneous identification of time-varying structural parameters and unknown excitations of a building structure , 2015 .

[24]  Mahmood Md. Tahir,et al.  Seismic and Progressive Collapse Assessment of New Proposed Steel Connection , 2015 .

[25]  Arezou Shafaghat,et al.  Waterproof performance of concrete: A critical review on implemented approaches , 2015 .

[26]  Mahdi Shariati,et al.  Seismic performance of structures with pre-bent strips as a damper , 2012 .

[27]  Mohammad Ismail,et al.  Performance of natural rubber latex modified concrete in acidic and sulfated environments , 2012 .

[28]  B. Bradley,et al.  Development of an empirical correlation for predicting shear wave velocity of Christchurch soils from cone penetration test data , 2015 .

[29]  Eugenio Oñate,et al.  An empirical comparison of machine learning techniques for dam behaviour modelling , 2015 .

[30]  M. Nilsson,et al.  Design of electrorheological dampers by means of finite element analysis: theory and applications , 2002 .

[31]  Taher Baghaee Moghaddam,et al.  Analysis of fatigue properties of unmodified and polyethylene terephthalate modified asphalt mixtures using response surface methodology , 2015 .

[32]  A. H. Al-Saidy,et al.  Structural behavior of corroded RC beams with/without stirrups repaired with CFRP sheets , 2015, Materials and Structures.

[33]  Shahaboddin Shamshirband,et al.  Estimation of the rutting performance of Polyethylene Terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology , 2015 .

[34]  W. Faris,et al.  Investigation of the performance of a rotor-bearing system containing composite and non-composite squeeze film dampers , 2012 .

[35]  Kosuke Nagaya,et al.  Development on 2DOF-type and Rotary-type shock absorber damper using MRF and their efficiencies , 2005 .