KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Model for Selecting Superstructure Type of Small Size Bridge Using Dual Classification Method

이원분류기법을 이용한 소규모 교량 상부형식선정 모형에 관한 연구

  • 윤수영 (경상대학교 토목공학과) ;
  • 김창학 (경남과학기술대학교 토목공학과) ;
  • 강인석 (경상대학교 토목공학과)
  • Received : 2014.11.24
  • Accepted : 2015.09.25
  • Published : 2015.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

On the design phase of small size bridge, owing to the lack of related guidelines or standards to determine a superstructure type of bridge, many designers tend to select the type depending on expert's experience and knowledge. Moreover, recently, as types of bridge superstructure become diverse and more conditions need to be considered in the project, the decision makes process become complex. This research covered the selection of a superstructure type of a middle or small size bridge with span length of about 50m, which frequently built for national roadway, selecting type of bridge superstructure more systematic way rather than the existing ways to compare construction methods or to depend on expert's experiences. This study proposes to build a bridge superstructure type selection model using one of the techniques of artificial intelligence techniques SVM by applicability of the model examined through the verification of the actual case.

중 소규모의 교량 설계단계에서는 교량 상부형식을 결정할 수 있는 관련 기준이 미흡한 관계로 설계자들은 일부 전문 기술자의 경험과 지식에 의존하여 교량상부형식을 선정하는 경향이 많다. 또한, 최근에는 교량상부형식이 매우 다양해지고 있으며, 고려해야할 조건들이 많아짐에 따라 의사결정과정도 더욱 복잡화되고 있다. 본 연구에서는 국도공사 등에 빈번히 시공되는 경간장 50m 내외의 중 소교량의 상부형식 선정을 위해 기존의 통상적인 현장에 적용가능한 공법의 비교방식 및 경험과 직관에 의존한 방법이 아닌 보다 체계적인 방법으로 교량상부형식을 선정하고자 한다. 이에 인공지능 기법중 하나인 SVM기법을 이용한 교량상부형식 선정 모형을 구축하여 제안하고, 실제사례의 검증을 통해 모형의 적용가능성을 검토하였다.

Keywords

References

  1. Bae, K. C. (2005). Recognition of superimposed patterns with selective attention based on SVM. Ph.M. Dissertation, University of KAIST, Daejeon (in Korean).
  2. Burges, C. J. C. (1988). "A tutorial on support vector machines for pattern recognition." Journal of, Knowledge Discovery and Data Mining, Vol. 2, No. 2, pp. 121-167.
  3. Choi, C. K. and Choi, I. H. (1992). "Development of expert system for a preliminary bridge design." Journal of Korean Society of Civil Engineers, Vol. 12, No. 1, pp. 9-17.
  4. Chung, Y. M. and Lim, H. Y. (2000). "An experimental study on text categorization using an SVM classifier." Journal of Korea Society for Information Management, Vol. 17, No. 4, pp. 229-248.
  5. Dumais, S., Platt, J., Heckerman, D. and Sahami, M. (1998). "Inductive learning algorithms and representations for text categorization." Proc. of Conf, The Seventh International Conference on Information and Knowledge Management, pp. 148-155.
  6. Lee, J. S., Kim, Y. W. and Oh, I. S. (2005). "Performance comparison of SVM and neural networks for large-set classification problems." Journal of Korea Information Processing Society, Vol. 12, No. 1, pp. 25-30.
  7. Park, U. Y. and Kim, J. Y. (2006). "A study on the selection model of retaining wall methods using support vector machines." Journal of Korea Institute of Construction Engineering and Management, Vol. 7, No. 2, pp. 118-126.
  8. Park. U. Y., An, S. H. and Kang, K. I. (2006). "A study on the assessment model of preliminary cost estimates using support vector machines." Journal of Architectural Institute of Korea, Vol. 21, No. 12, pp. 191-198.
  9. Vapnik, V. N. (1999). "The nature of statistical learning theory" Springer-Verlag.