Browse > Article

Prediction of Ground Condition and Evaluation of its Uncertainty by Simulated Annealing  

Ryu Dong-Woo (한국지질자원연구원)
Publication Information
Tunnel and Underground Space / v.15, no.4, 2005 , pp. 275-287 More about this Journal
Abstract
At the planning and design stages of a development of underground space or tunneling project, the information regarding ground conditions is very important to enhance economical efficiency and overall safety In general, the information can be expressed using RMR or Q-system and with the geophysical exploration image. RMR or Q-system can provide direct information of rock mass in a local scale for the design scheme. Oppositely, the image of geophysical exploration can provide an exthaustive but indirect information. These two types of the information have inherent uncertainties from various sources and are given in different scales and with their own physical meanings. Recently, RMR has been estimated in unsampled areas based on given data using geostatistical methods like Kriging and conditional simulation. In this study, simulated annealing(SA) is applied to overcome the shortcomings of Kriging methods or conditional simulations just using a primary variable. Using this technique, RMR and the image of geophysical exploration can be integrated to construct the spatial distribution of RM and to evaluate its uncertainty. The SA method was applied to solve an optimization problem with constraints. We have suggested the practical procedure of the SA technique for the uncertainty evaluation of RMR and also demonstrated this technique through an application, where it was used to identify the spatial distribution of RMR and quantify the uncertainty. For a geotechnical application, the objective functions of SA are defined using statistical models of RMR and the correlations between RMR and the reference image. The applicability and validity of this application are examined and then the result of uncertainty evaluation can be used to optimize the tunnel layout.
Keywords
RMR; Simulated annealing(SA); Spatial distribution; Uncertainty; Optimization;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 홍창우, 전석원, 2004, 유전알고리즘을 이용한 암반 물성의 최적 평가에 관한 연구, 터널과 지하공간, 15.2, 129-136
2 Aarts, E. and Korst, I., 1989, Simulated Annealing and Boltzmann Machines - a Stochastic Approach to Combinatorial Optimization and Neural Computing, Wiley, New York, 272 p
3 Deutsch, C. V. and P.W. Cockerham, 1994, Practical consderations in the application of simulated annealing to stochastic simulation, Mathematical Geology, 26.1, 67-82   DOI   ScienceOn
4 Ouenes, A., S. Bhagavan, P.H. Bunge and B.J. Travis, 1994, Application of simulated annealing and other global optimization methods to reservoir description: Myths and realities, 69th Annual Technical Conference and Exhibition of the Society of Petroleum Engineers, 547-561
5 유광호, 이상호, 추석연, 주광수, 2004, 터널 중심선으로부터 이격된 자료를 활용한 미시추구간의 암반등급 산정에 관한 연구, 터널기술, 6.2, 101-111
6 류동우, 김택곤, 허종석, 2003, RMR의 불확실성 모델링을 위한 지구통계학적 시뮬레이션 기법에 관한 연구, 터널과 지하공간, 12.3, 87-99
7 Deutsch, C.V. and Journel, A.G., 1998, GSLIB Geostatistical Software and User's Guide, Oxford Univ. Press, New York. 369 p
8 유광호, 2003, 터널 설계를 위한 암반등급 산정 기법에 관한 연구, 한국지반공학회논문집, 19.5, 103-106
9 Langlais, V. and I. Doyle, 1993, Comparisons of several methods of lithofacies simulation on the fluvial gypsy sandstone of Oklahma, In Geostatistics Troia '92, A. Soares, ed., Kluwer, 1, 299-310
10 Kirkpatrick, S., Gellat, C.D. and Vecchi, M.P., 1983, Optimization by simulated annealing, Science 220, 671-680   DOI   PUBMED   ScienceOn