Browse > Article

Application of Artificial Neural Networks for Prediction of the Unconfined Compressive Strength (UCS) of Sedimentary Rocks in Daegu  

Yim Sung-Bin (Dept. of Geology, Kyungpook National University)
Kim Gyo-Won (Dept. of Geology, Kyungpook National University)
Seo Yong-Seok (Dept. of Earth and Environmental Sci.ㆍInstitute for Basic Sciences, Chungbuk National University)
Publication Information
The Journal of Engineering Geology / v.15, no.1, 2005 , pp. 67-76 More about this Journal
Abstract
This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.
Keywords
artificial neural network; back-propagation teaming algorithm; unconfined compressive strength; schmidt hardness number; sedimentary rock;
Citations & Related Records
연도 인용수 순위
  • Reference
1 김대수, 1992, 신경망 이론과 응용(I), 하이테크 정보
2 김현우, 김영근, 이희근, 1999, 인공신경망을 이용한 터 널 건전도 평가시스템 개발, 터널과 지하공간 Vol.9, pp.48-55
3 이인모, 조계춘, 이정학, 1997, 인공신경망을 이용한 암 반의 투수계수 예측, 한국지반공학회논문집, Vol.13, No.2, pp.77-89
4 Singh, V. K., Singh D. and Singh, T. N., 2001, Prediction of strength properties of some schistose rock from petrographic properties using artificial neural networks, Int. J. Rock Mech. Min. Sci., Vol.38, pp.269-284   DOI   ScienceOn
5 Toll, D., 1996, Artificial Intelligence Application in Geotechnical Engineering, Electronic journal of Geotechnical Engineering, Premiere Issue
6 Eberhart, R. C., and Dobbins, R. W., 1990, Neural Network PC Tools, Academic Press Inc
7 김남수, 양형식, 1996, 가속신경망에 의한 암반물성의 추정, 터널과 지하공간, Vol.6, pp.316-325
8 Meulenkamp, F. and Alvarez, G. M., 1999, Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness, Int. J. Rock Mech. Min. Sci., Vol.36, pp.29-39   DOI   ScienceOn
9 Huang, Y. and W˝a nstedt, S., 1998, The introduction of neural network system and its applications in rock engineering, Engineering Geology, Vol.49, pp.253-260   DOI   ScienceOn
10 Zhang, Q., Song, J. and Nie X., 1991, Application of Neural Network Models to Rock Mechanics and Rock Engineering, Int. J. Rock Mech. Min. Sci. & Geomech. Abstr., Vol.28, pp.535-540   DOI   ScienceOn
11 김영수, 김동락, 이상웅, 허노영, 2003, 인공신경망을 이용한 암석의 강도 예측, 대한토목학회 정기학술대 회, pp.4747-4751
12 Yang, Y. and Zhang, Q., 1998, The Application of neural networks to Rock Engineering System (RES), Int. J. Rock Mech. Min. Sci., Vol.35, pp.727-745   DOI   ScienceOn
13 양형식, 김재철, 1999, 인공신경망을 이용한 한국형 터 널 암반분류, 터널과 지하공간, Vol.9, pp.214-220
14 Yuanyou, X., Yanming, X. and Ruigeng, Z. R., 1997, An engineering geology evaluation method based on an artificial neural network and its application, Engineering Geology, Vol.47, pp.149-156   DOI   ScienceOn