Advanced SearchSearch Tips
Development of Model for Selecting Superstructure Type of Small Size Bridge Using Dual Classification Method
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Development of Model for Selecting Superstructure Type of Small Size Bridge Using Dual Classification Method
Yun, Su Young; Kim, Chang Hak; Kang, Leen Seok;
  PDF(new window)
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.
SVM (Support Vector Machine);Bridge superstructure;Alternative selection;Dual classification method;
 Cited by
Bae, K. C. (2005). Recognition of superimposed patterns with selective attention based on SVM. Ph.M. Dissertation, University of KAIST, Daejeon (in Korean).

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.

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.

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.

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.

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.

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.

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.

Vapnik, V. N. (1999). "The nature of statistical learning theory" Springer-Verlag.