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Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning
The Annex D in the latest edition of the international standard General Principles on Reliability for Structures (ISO2394:2015) recognizes that the geotechnical reliability-based design should place site investigation and the interpretation of site conditions as the cornerstone of the methodology. The geotechnical reliability analysis is sound only when the source uncertainty—the interpretation of site conditions—is well quantified. It is a consensus that probabilistic subsurface stratification is still an open question in geotechnical engineering. The gap lies in the difficulty of developing an uncertainty-aware integration of subjective engineering judgment and the sparse objective site exploration data. In this investigation, a stratigraphic uncertainty quantification approach is developed by integrating the Markov random field (MRF) model and the discriminant adaptive nearest neighbor-based k-harmonic mean distance (DANN-KHMD) classifier into a Bayesian framework. To demonstrate the effectiveness, both synthetic and real-world examples are illustrated.
Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning
The Annex D in the latest edition of the international standard General Principles on Reliability for Structures (ISO2394:2015) recognizes that the geotechnical reliability-based design should place site investigation and the interpretation of site conditions as the cornerstone of the methodology. The geotechnical reliability analysis is sound only when the source uncertainty—the interpretation of site conditions—is well quantified. It is a consensus that probabilistic subsurface stratification is still an open question in geotechnical engineering. The gap lies in the difficulty of developing an uncertainty-aware integration of subjective engineering judgment and the sparse objective site exploration data. In this investigation, a stratigraphic uncertainty quantification approach is developed by integrating the Markov random field (MRF) model and the discriminant adaptive nearest neighbor-based k-harmonic mean distance (DANN-KHMD) classifier into a Bayesian framework. To demonstrate the effectiveness, both synthetic and real-world examples are illustrated.
Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning
Wang, Hui (author) / Wei, Xingxing (author)
Geo-Congress 2022 ; 2022 ; Charlotte, North Carolina
Geo-Congress 2022 ; 337-346
2022-03-17
Conference paper
Electronic Resource
English
Stochastic Stratigraphic Simulation and Uncertainty Quantification Using Machine Learning
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