Structural Engineering and Mechanics

  • 제60권3호
  • /
  • Pages.471-488
  • /
  • 2016
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Strength prediction of rotary brace damper using MLR and MARS

  • Mansouri, I. (Deptartment of Civil Engineering, Birjand University of Technology) ;
  • Safa, M. (Deptartment of Civil Engineering, University of Malaya) ;
  • Ibrahim, Z. (Deptartment of Civil Engineering, University of Malaya) ;
  • Kisi, O. (Center for Interdisciplinary Research, International Black Sea University) ;
  • Tahir, M.M. (UTM CRC, Institute for Smart Infrastructure and Innovative Construction, UTM) ;
  • Baharom, S. (Department of Civil and Structural Engineering, National University of Malaysia) ;
  • Azimi, M. (Department of Quantity Surveying, Universiti Teknologi Malaysia)
  • 투고 : 2015.10.25
  • 심사 : 2016.09.06
  • 발행 : 2016.11.10
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Higher Education, Malaysia, University Malaya, Ministry of Education, Malaysia

참고문헌

  1. Abdulrahman, A., Ismail, M. and Hussain, M.S. (2011), "Inhibition of corrosion of mild steel in hydrochloric acid by Bambusa arundinacea", Int. Rev. Mech. Eng., 5(1), 59-63.
  2. Alhajri, T., Tahir, M., Azimi, M., Mirza, J., Lawan, M., Alenezi, K. and Ragaee, M. (2016), "Behavior of pre-cast U-shaped composite beam integrating cold-formed steel with ferro-cement slab", Thin Wall. Struct., 102, 18-29. https://doi.org/10.1016/j.tws.2016.01.014
  3. Ataei, A., Bradford, M.A. and Valipour, H.R. (2015), "Experimental study of flush end plate beam-to-CFST column composite joints with deconstructable bolted shear connectors", Eng. Struct., 99, 616-630. https://doi.org/10.1016/j.engstruct.2015.05.012
  4. Azimi, M., Adnan, A.B., Osman, M.H., Sam, A.R.B.M., Faridmehr, I. and Hodjati, R. (2014), "Energy absorption capacity of reinforced concrete beam-column connections, with ductility classes low", Am. J. Civil Eng. Arch., 2(1), 42-52. https://doi.org/10.12691/ajcea-2-1-5
  5. Azimi, M., Adnan, A.B., Tahir, M.M., Sam, A.R.B.M. and Razak, S.M.B.S.A. (2015), "Seismic performance of ductility classes medium RC beam-column connections with continuous rectangular spiral transverse reinforcements", Lat. Am. J. Solid. Struct., 12(4), 787-807. https://doi.org/10.1590/1679-78251387
  6. Azimi, M., Ponraj, M., Bagherpourhamedani, A., Tahir, M.M., Razak, S.M.S.A. and Pheng, O.P. (2015), "Shear capacity evaluation of reinforced concrete beams: finite element simulation", J. Teknologi, 77(16).
  7. Chan, R. and Wong, P. (2014), "A passive rotary system for seismic risk mitigation of steel structures", Australasian Journal of Construction Economics and Building-Conference Series.
  8. Daie, M., Jalali, A., Suhatril, M., Shariati, M., Arabnejad Khanouki, M.M., Shariati, A. and Kazemi Arbat, P. (2011), "A new finite element investigation on pre-bent steel strips as damper for vibration control", Int. J. Phys. Sci., 6(36), 8044-8050.
  9. Els, P.S. and Holman, T.J. (1999), "Semi-active rotary damper for a heavy off-road wheeled vehicle", J. Terramech., 36(1), 51-60. https://doi.org/10.1016/S0022-4898(98)00024-X
  10. Faridmehr, I., Osman, M.H., Tahir, M.M., Nejad, A.F. and Azimi, M. (2015), "Seismic and progressive collapse assessment of new proposed steel connection", Adv. Struct. Eng., 18(3), 439-452. https://doi.org/10.1260/1369-4332.18.3.439
  11. Faris, W.F., Khalid, A.A., Albagul, A. and Ismail, G.A. (2012), "Investigation of the performance of a rotorbearing system containing composite and non-composite squeeze film dampers", Mater. Des., 34, 340-345. https://doi.org/10.1016/j.matdes.2011.07.053
  12. Friedman, J. H. (1991), "Multivariate adaptive regression splines", Ann. Statist., 1-67.
  13. Hafizah, N., Bhutta, M., Jamaludin, M., Warid, M., Ismail, M., Rahman, M., Yunus, I. and Azman, M. (2014), "Kenaf fiber reinforced polymer composites for strengthening RC Beams", J. Adv. Concrete Tech., 12(6), 167-177. https://doi.org/10.3151/jact.12.167
  14. Heysami, A. (2015), "Types of dampers and their seismic performance during an earthquake", Curr. World Environ., 10, 1002-1015. https://doi.org/10.12944/CWE.10.Special-Issue1.119
  15. Hibbitt, K., Karlsson, B. and Sorensen, P. (1988), ABAQUS: User's Manual, Hibbitt, Karlsson & Sorensen.
  16. Huang, L.J., Sheen, Y.N. and Le, D.H. (2014), "On the multiple linear regression and artificial neural networks for strength prediction of soil-based controlled low-strength material", Applied Mechanics and Materials, Trans Tech Publication.
  17. Huang, Q., Xu, Y. and Liu, H. (2015), "An efficient algorithm for simultaneous identification of timevarying structural parameters and unknown excitations of a building structure", Eng. Struct., 98, 29-37. https://doi.org/10.1016/j.engstruct.2015.04.019
  18. Imaduddin, F., Mazlan, S.A. and Zamzuri, H. (2013), "A design and modelling review of rotary magnetorheological damper", Mater. Des., 51, 575-591. https://doi.org/10.1016/j.matdes.2013.04.042
  19. Jalali, A., Daie, M., Nazhadan, S.V.M., Kazemi-Arbat, P. and Shariati, M. (2012), "Seismic performance of structures with pre-bent strips as a damper", Int. J. Phys. Sci., 7(26), 4061-4072.
  20. Kang, J.D. and Tagawa, H. (2013), "Seismic response of steel structures with seesaw systems using viscoelastic dampers", Earthq. Eng. Struct. Dyn., 42(5), 779-794. https://doi.org/10.1002/eqe.2244
  21. Kawamoto, M., Yamaguchi, K. and Tokahashi, N. (1993), Rotary damper with improved connection between cap and housing, Google Patents.
  22. Khorramian, K., Maleki, S., Shariati, M. and Ramli Sulong, N.H. (2015), "Behavior of Tilted Angle Shear Connectors", PLoS ONE, 10(12), e0144288. https://doi.org/10.1371/journal.pone.0144288
  23. Mansouri, I. and Kisi, O. (2015), "Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches", Compos. Part B: Eng., 70, 247-255. https://doi.org/10.1016/j.compositesb.2014.11.023
  24. McGann, C.R., Bradley, B.A., Taylor, M.L., Wotherspoon, L.M. and Cubrinovski, M. (2015), "Development of an empirical correlation for predicting shear wave velocity of Christchurch soils from cone penetration test data", Soil Dyn. Earthq. Eng., 75, 66-75. https://doi.org/10.1016/j.soildyn.2015.03.023
  25. Moghaddam, T.B., Soltani, M., Karim, M.R., Shamshirband, S., Petkovic, D. and Baaj, H. (2015), "Estimation of the rutting performance of Polyethylene Terephthalate modified asphalt mixtures by adaptive neuro-fuzzy methodology", Constr. Build. Mater., 96, 550-555. https://doi.org/10.1016/j.conbuildmat.2015.08.043
  26. Moghaddam, T.B., Soltani, M., Shahraki, H.S., Shamshirband, S., Noor, N.B.M. and Karim, M.R. (2016), "The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures", Measur., 90, 526-533.
  27. Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M., Jameel, M., Hakim, S. and Zargar, M. (2013), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struct. Eng. Mech., 45(3), 319-332.
  28. Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M. and Shariati, M. (2013), "Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams", Struct. Eng. Mech., 46(6), 853-868. https://doi.org/10.12989/sem.2013.46.6.853
  29. Mohammadhassani, M., Suhatril, M., Shariati, M. and Ghanbari, F. (2014), "Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios", Struct. Eng. Mech., 48(6), 833-848. https://doi.org/10.12989/SEM.2013.48.6.833
  30. Muhammad, B. and Ismail, M. (2012), "Performance of natural rubber latex modified concrete in acidic and sulfated environments", Constr. Build. Mater., 31, 129-134. https://doi.org/10.1016/j.conbuildmat.2011.12.099
  31. Muhammad, B., Ismail, M., Bhutta, M.A.R. and Abdul-Majid, Z. (2012), "Influence of non-hydrocarbon substances on the compressive strength of natural rubber latex-modified concrete", Constr. Build. Mater., 27(1), 241-246. https://doi.org/10.1016/j.conbuildmat.2011.07.054
  32. Muhammad, N.Z., Keyvanfar, A., Majid, M.Z.A., Shafaghat, A. and Mirza, J. (2015), "Waterproof performance of concrete: A critical review on implemented approaches", Constr. Build. Mater., 101, 80-90. https://doi.org/10.1016/j.conbuildmat.2015.10.048
  33. Muhammad, N.Z., Shafaghat, A., Keyvanfar, A., Majid, M.Z.A., Ghoshal, S.,. Yasouj, S.E.M., Ganiyu, A.A., Kouchaksaraei, M.S., Kamyab, H. and Taheri, M.M. (2016), "Tests and methods of evaluating the self-healing efficiency of concrete: A review", Constr. Build. Mater., 112, 1123-1132. https://doi.org/10.1016/j.conbuildmat.2016.03.017
  34. Noresson, V., Ohlson, N.G. and Nilsson, M. (2002), "Design of electrorheological dampers by means of finite element analysis: theory and applications", Mater. Des., 23(4), 361-369. https://doi.org/10.1016/S0261-3069(02)00006-7
  35. Pranoto, T. and Nagaya, K. (2005), "Development on 2DOF-type and Rotary-type shock absorber damper using MRF and their efficiencies", J. Mater. Pr. Tech., 161(1-2), 146-150. https://doi.org/10.1016/j.jmatprotec.2004.07.018
  36. Salazar, F., Toledo, M., Onate, E. and Moran, R. (2015), "An empirical comparison of machine learning techniques for dam behaviour modelling", Struct. Saf., 56, 9-17. https://doi.org/10.1016/j.strusafe.2015.05.001
  37. Samui, P. and Kim, D. (2013), "Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles", Neural Comput. Appl., 23(3-4), 1123-1127. https://doi.org/10.1007/s00521-012-1043-x
  38. Scholl, R.E. (1984), "Brace dampers: an alternative structural system for improving the earthquake performance of buildings", Proceedings of 8th World Conference on Earthquake Engineering, San Francisco.
  39. Schumacher, T. and Shariati, A. (2013), "Monitoring of structures and mechanical systems using virtual visual sensors for video analysis: Fundamental concept and proof of feasibility", Sens., 13(12), 16551-16564. https://doi.org/10.3390/s131216551
  40. Shariati, A. and Schumacher, T. (2015), "Oversampling in virtual visual sensors as a means to recover higher modes of vibration", 41st Annual Review of Progress in Quantitative Nondestructive Evaluation, 34, AIP Publishing.
  41. Shariati, A., Schumacher, T. and Ramanna, N. (2015), "Eulerian-based virtual visual sensors to detect natural frequencies of structures", J. Civil Struct. Hlth. Monit., 5(4), 457-468. https://doi.org/10.1007/s13349-015-0128-5
  42. Shariati, M., Ramli Sulong, N.H. and Arabnejad Khanouki, M.M. (2010), "Experimental and analytical study on channel shear connectors in light weight aggregate concrete", Proceedings of the 4th International Conference on Steel & Composite Structures, Sydney, Australia, July.
  43. Shariati, M., Ramli Sulong, N.H., Arabnejad Khanouki, M.M. and Mahoutian, M. (2011), "Shear resistance of channel shear connectors in plain, reinforced and lightweight concrete", Sci. Res. Essay., 6(4), 977-983.
  44. Shariati, M., Ramli Sulong, N.H., Shariati, A. and Khanouki, M.A. (2015), "Behavior of V-shaped angle shear connectors: experimental and parametric study", Mater. Struct., 49(9), 1-18.
  45. Shariati, M., Ramli Sulong, N.H., Sinaei, H., Arabnejad Khanouki, M.M. and Shafigh, P. (2011), "Behavior of channel shear connectors in normal and light weight aggregate concrete (experimental and analytical study)", Adv. Mater. Res., 168, 2303-2307.
  46. Shariati, M., Sulong, N.R., Suhatril, M., Shariati, A., Khanouki, M.A. and Sinaei, H. (2012), "Behaviour of C-shaped angle shear connectors under monotonic and fully reversed cyclic loading: An experimental study", Mater. Des., 41, 67-73. https://doi.org/10.1016/j.matdes.2012.04.039
  47. Silva, A., Neves, R. and de Brito, J. (2014), "Statistical modelling of carbonation in reinforced concrete", Cement Concrete Compos., 50, 73-81. https://doi.org/10.1016/j.cemconcomp.2013.12.001
  48. Sinaei, H., Shariati, M., Abna, A., Aghaei, M. and Shariati, A. (2012), "Evaluation of reinforced concrete beam behaviour using finite element analysis by ABAQUS", Sci. Res. Essay., 7(21), 2002-2009.
  49. Soltani, M., Moghaddam, T.B., Karim, M.R. and Baaj, H. (2015), "Analysis of fatigue properties of unmodified and polyethylene terephthalate modified asphalt mixtures using response surface methodology", Eng. Fail. Anal., 58, 238-248. https://doi.org/10.1016/j.engfailanal.2015.09.005
  50. Symans, M., Charney, F., Whittaker, A., Constantinou, M., Kircher, C., Johnson, M. and McNamara, R. (2008), "Energy dissipation systems for seismic applications: current practice and recent developments", J. Struct. Eng., 134(1), 3-21. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:1(3)
  51. Tahmasbi, F., Maleki, S., Shariati, M., Sulong, N.R. and Tahir, M. (2016), "Shear capacity of C-shaped and L-shaped angle shear connectors", PloS one, 11(8), e0156989. https://doi.org/10.1371/journal.pone.0156989
  52. Umemura, H. (1994), Rotary oil damper, Google Patents.
  53. Yazid, I.I.M., Mazlan, S.A., Kikuchi, T., Zamzuri, H. and Imaduddin, F. (2014), "Design of magnetorheological damper with a combination of shear and squeeze modes", Mater. Des., 54, 87-95. https://doi.org/10.1016/j.matdes.2013.07.090
  54. Zhang, W. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016

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