Structural Engineering and Mechanics

  • 제45권5호
  • /
  • Pages.693-702
  • /
  • 2013
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A novel regression prediction model for structural engineering applications

  • Lin, Jeng-Wen (Department of Civil Engineering, Feng Chia University) ;
  • Chen, Cheng-Wu (Department of Maritime Information and Technology, National Kaohsiung Marine University) ;
  • Hsu, Ting-Chang (Department of Civil Engineering, Feng Chia University)
  • 투고 : 2012.07.01
  • 심사 : 2013.02.19
  • 발행 : 2013.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, artificial intelligence tools are most used for structural engineering and mechanics. In order to predict reserve prices and prices of awards, this study proposed a novel regression prediction model by the intelligent Kalman filtering method. An artificial intelligent multiple regression model was established using categorized data and then a prediction model using intelligent Kalman filtering. The rather precise construction bid price model was selected for the purpose of increasing the probability to win bids in the simulation example.

키워드

참고문헌

  1. Bubshalt, A.A. and Al-Gobali, K.H. (1996), "Contractor prequalification in Saudi Arabia," Journal of Management in Engineering, ASCE, 12(2), 50-54.
  2. Chen, C.W., Wang, M.H.L. and Lin, J.W. (2009), "Managing target the cash balance in construction firms using a fuzzy regression approach", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17(5), 667-684. https://doi.org/10.1142/S0218488509006200
  3. Edum-Fotwe, F., Price, A. and Thorpe, A. (1996), "A review of financial ratio tools for contractor insolvency", Construction Management and Economics, 14, 189-198. https://doi.org/10.1080/014461996373458
  4. Esfahanipour, A. and Aghamiri, W. (2010), "Adapted neuro-fuzzy inference system on indirect approach TSK fuzzy rule base for stock market analysis", Expert Systems with Applications, 37, 4742-4748. https://doi.org/10.1016/j.eswa.2009.11.020
  5. Hong, J.Y. (2007), "Fuzzy neural network model for estimating project indirect cost ratio", Master Thesis, Department of Construction Engineering, National Kaohsiung First University of Science and Technology, Taiwan. (In Chinese)
  6. Hsiao, F.H., Chiang, W.L. and Chen, C.W. (2003), "Application and fuzzy $H^{{\infty}}$ control via T-S fuzzy models for nonlinear time-delay systems", International Journal on Artificial Intelligence Tools, 12(2), 117-137. https://doi.org/10.1142/S0218213003001174
  7. Kim, C.W., Kawatani, M., Ozaki, R. and Makihata, N. (2011), "Recovering missing data transmitted from a wireless sensor node for vibration-based bridge health monitoring", Structural Engineering and Mechanics, 38(4), 417-428. https://doi.org/10.12989/sem.2011.38.4.417
  8. Klir, G.J. and Yuan, B. (1995), Fuzzy Sets and Fuzzy Logic Theory and Applications, Prentic-Hall, Englewood Cliffs, NJ.
  9. Li, S.J., Suzuki, Y. and Noori, M.N. (2004), "Improvement of parameter estimation for non-linear hysteretic systems with slip by a fast Bayesian bootstrap filter", International Journal of Nonlinear Mechanics, 39(9), 1435-1445. https://doi.org/10.1016/j.ijnonlinmec.2004.02.005
  10. Lin, J.W. (2001), "Adaptive algorithms for the identification of nonlinear structural systems", Ph.D. Dissertation, Columbia University, New York.
  11. Lin, J.W. (2012), "Fuzzy regression decision systems for assessment of the potential vulnerability of bridge to earthquakes", Natural Hazards, 64(1), 211-221. https://doi.org/10.1007/s11069-012-0230-5
  12. Lin, J.W., Betti, R., Smyth, A.W. and Longman, R.W. (2001), "On-line identification of non-linear hysteretic structural systems using a variable trace approach", Earthquake Engineering and Structural Dynamics, 30(9), 1279-1303. https://doi.org/10.1002/eqe.63
  13. Lin, J.W. and Betti, R. (2004), "On-line identification and damage detection in non-linear structural systems using a variable forgetting factor approach", Earthquake Engineering and Structural Dynamics, 33(4), 419-444. https://doi.org/10.1002/eqe.350
  14. Lin, J.W., Chen, C.W. and Hsu, T.C. (2012), "Fuzzy statistical refinement for the forecasting of tenders for roadway construction", Journal of Marine Science and Technology, 20(4), 410-417.
  15. Lin, J.W. and Chen, H.J. (2009), "Repetitive identification of structural systems using a nonlinear model parameter refinement approach", Shock and Vibration, 16(3), 229-240. https://doi.org/10.1155/2009/174917
  16. Mikut, R., Jäkel, J., Groll, L. (2005), "Interpretability issues in data-based learning of fuzzy systems", Fuzzy Sets and Systems, 150(2), 179-197. https://doi.org/10.1016/j.fss.2004.06.006
  17. Niimura, T. and Nakashima, T. (2001), "Deregulated electricity market data representation by fuzzy regression models", IEEE Trans. Syst., Man, Cybern., 31, 320-326. https://doi.org/10.1109/5326.971659
  18. Russell, J.S. and Skibniewwski, M.J. (1988), "Decision criteria in contractor prequalification", Journal of Management in Engineering, ASCE, 4(2), 148-164. https://doi.org/10.1061/(ASCE)9742-597X(1988)4:2(148)
  19. Shakouri, H., Nadimi, R., and Ghaderi, F. (2009), "A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T-S fuzzy neural network", Expert Systems with Applications, 36, 2377-2390. https://doi.org/10.1016/j.eswa.2007.12.068
  20. Smyth, A.W., Masri, S.F., Chassiakos, A.G. and Caughey, T.K. (1999), "On-line parametric identification of MDOF nonlinear hysteretic systems", Journal of Engineering Mechanics, 125, 133-142. https://doi.org/10.1061/(ASCE)0733-9399(1999)125:2(133)
  21. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE Transactions on Systems, Man and Cybernetics, 15(1), 116-132.
  22. Tarawneh, S.A. (2004), "Evaluation of pre-qualification criteria: client perspective; Jordan case study", Journal of Applied Sciences, 4(3), 354-363. https://doi.org/10.3923/jas.2004.354.363
  23. Xu, Y. and Chen, J. (2012), "Fuzzy control for geometrically nonlinear vibration of piezoelectric flexible plates", Structural Engineering and Mechanics, 43(2), 163-177. https://doi.org/10.12989/sem.2012.43.2.163
  24. Yan, Z.X. (2006), "Application of fuzzy set theory for evaluation of construction alternatives for private schools", Master Thesis, Department of Construction Engineering, National Kaohsiung First University of Science and Technology, Taiwan. (In Chinese)
  25. Zadeh, L.A. (1965), "Fuzzy sets", Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

피인용 문헌

  1. Repetitive model refinement for structural health monitoring using efficient Akaike information criterion vol.15, pp.5, 2015, https://doi.org/10.12989/sss.2015.15.5.1329
  2. Detection and parametric identification of structural nonlinear restoring forces from partial measurements of structural responses vol.54, pp.2, 2015, https://doi.org/10.12989/sem.2015.54.2.291
  3. Experimental modeling and evaluation of the afterglow phosphors using multiple single exponential equations vol.148, 2015, https://doi.org/10.1016/j.neucom.2014.06.064
  4. Online estimation of noise parameters for Kalman filter vol.47, pp.3, 2013, https://doi.org/10.12989/sem.2013.47.3.361
  5. Factor-analysis based questionnaire categorization method for reliability improvement of evaluation of working conditions in construction enterprises vol.51, pp.6, 2014, https://doi.org/10.12989/sem.2014.51.6.973