Uncertainty reduction and sampling efficiency in slope designs using 3D conditional random fields |
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| Affiliation: | 1. ARC Centre of Excellence for Geotechnical Science and Engineering, The University of Newcastle, NSW, Australia;2. Colorado School of Mines, Golden, CO, USA;1. Section of Geo-Engineering, Department of Geoscience and Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, PR China;1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering (Ministry of Education), Wuhan University, 8 Donghu South Road, Wuhan 430072, PR China;2. Department of Civil and Environmental Engineering, National University of Singapore, Blk E1A, #07-03, 1 Engineering Drive 2, Singapore 117576, Singapore;3. School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, PR China;1. School of Civil Engineering and Architecture, Nanchang University, 999 Xuefu Road, Nanchang 330031, PR China;2. Engineering Risk Analysis Group, Technische Universität München, 80333 Munich, Germany;3. ARC Centre of Excellence for Geotechnical Science and Engineering, The University of Newcastle, NSW, Australia;1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 8 Donghu South Road, Wuhan 430072, PR China;2. Department of Civil and Environmental Engineering, National University of Singapore, Blk E1A, #07-03, 1 Engineering Drive 2, Singapore 117576, Singapore |
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| Abstract: | A method of combining 3D Kriging for geotechnical sampling schemes with an existing random field generator is presented and validated. Conditional random fields of soil heterogeneity are then linked with finite elements, within a Monte Carlo framework, to investigate optimum sampling locations and the cost-effective design of a slope. The results clearly demonstrate the potential of 3D conditional simulation in directing exploration programmes and designing cost-saving structures; that is, by reducing uncertainty and improving the confidence in a project’s success. Moreover, for the problems analysed, an optimal sampling distance of half the horizontal scale of fluctuation was identified. |
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| Keywords: | Conditional random fields Kriging Reliability Sampling efficiency Spatial variability Uncertainty reduction |
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