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Bulletin of Engineering Geology and the Environment

Erschienen in:

01.04.2016 | Original Paper

Optimization of site investigation program for improved statistical characterization of geotechnical property based on random field theory

verfasst von: Wenping Gong, Yong-Ming Tien, C. Hsein Juang, James R. Martin II, Zhe Luo

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 3/2017

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Abstract

This paper presents a framework for optimization of site investigation program, within which the robustness of the site investigation program and the investigation effort are optimized. A site investigation program is judged robust if the derived statistics of the geotechnical property of interest are robust against the uncertainties caused by limited data availability and test error. In this study, a Markov chain Monte Carlo simulation-based Bayesian inference approach was used to characterize the statistics of the intended geotechnical property. The robustness of the site investigation program was formulated as a byproduct of the Bayesian inference of the geotechnical property statistics. The proposed framework for optimization of the site investigation program was implemented as a bi-objective optimization problem that considers both robustness and investigation effort. The concepts of Pareto Front and knee point were employed to aid in making an informed decision regarding selection of site investigation program. The effectiveness and significance of the proposed framework were demonstrated through a simulation study.

Vorheriger Artikel Hazard assessment of potentially dangerous bodies within a cliff based on the Fuzzy-AHP method: a case study of the Mogao Grottoes, China

Nächster Artikel The effect of weathering on durability and deformability properties of granitoid rocks

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Titel: Optimization of site investigation program for improved statistical characterization of geotechnical property based on random field theory
verfasst von: Wenping Gong
Yong-Ming Tien
C. Hsein Juang
James R. Martin II
Zhe Luo
Publikationsdatum: 01.04.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 3/2017
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-016-0869-3

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