Abstract
Geological heterogeneity is a result of the natural deposit process of strata with a highly irregular and uncertain distribution of soil mass in the real world. However, compared with the effect of inherent spatial variability of soil properties on geostructures, the effect of geological heterogeneity has received less attention so far. In view of this limitation, this paper adopts a Markov random field model to stochastically simulate the geological heterogeneity for assessing the bearing capacity of a shallow foundation with quantified uncertainty. By mapping the generated stratigraphic realizations in the commercial software FLAC3D version 7.0, an ensemble of numerical models is generated. Computational results show that geological heterogeneity has a significant influence on the estimated capacities. Information entropy is used to characterize the geological uncertainty. It is not surprising that the increase of boreholes could reduce the average information entropy, and thus reduce the standard deviation of bearing capacities. But the effect of increasing the borehole numbers on the uncertainty reduction exhibits a clear nonlinear form with a potential to select an optimal borehole number. Results from the proposed stochastic analysis provide a range of calculated capacities, which can be used to mitigate the risk of insufficient bearing capacity.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The support of the National Natural Science Foundation of China (Grant Nos. 52022070 and 51978516), the Peak Discipline Construction Project of Shanghai (No. 2021-CE-15) and Key Laboratory of Geotechnical and Underground Engineering (Tongji University), Ministry Education of China (KLE-TJGE-B1705) for this study is gratefully acknowledged. This work is also partially supported by the Ohio Department of Transportation under Agreement Number 31795 Subtask 6: A Study of AI-Based Methods for Characterization of Geotechnical Site Investigation Data, and by the STEM Catalyst Grant from the University of Dayton. The financial support from all sponsors is gratefully acknowledged. Additionally, the constructive comments of the anonymous reviewers are also appreciated.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 4 • December 2021
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© 2021 American Society of Civil Engineers.
History
Received: Jan 1, 2021
Accepted: Jun 17, 2021
Published online: Aug 25, 2021
Published in print: Dec 1, 2021
Discussion open until: Jan 25, 2022
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