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Characterizing geotechnical site investigation data: a comparative study using a novel distribution model
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Characterizing geotechnical site investigation data: a comparative study using a novel distribution model
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Journal Article
Characterizing geotechnical site investigation data: a comparative study using a novel distribution model
2023
Overview
The fitting of a parameterized distribution model to site investigation data is commonly adopted in geotechnical site characterization, but this exercise may require subjective interpretations from engineers. In the literature, many strategies, such as the Bayesian method, are proposed to facilitate objective selections of distribution model(s). In this paper, a novel distribution model, fractional moments-based maximum entropy distribution (MaxEnt-FM), is proposed to characterize site investigation data. Using two sets of simulated site investigation data that are of different characteristics, the MaxEnt-FM model is illustrated and compared with classic distribution models and the results obtained using the Bayesian model selection technique. A slope reliability analysis that utilizes the fitted distributions as inputs is then carried out to further compare the MaxEnt-FM model against other methods. The effects of the distribution model and the amount and variability of site investigation data on slope reliability are also discussed. The results indicate that the Bayesian method may provide biased model selections, which may result in inaccurate slope reliability calculations. In contrast, the MaxEnt-FM model not only reasonably characterizes the site investigation data, but also offers a method that is sufficiently generalizable and requires minimum interventions from engineers to handle site investigation data that is of different quantity and quality. Finally, the effectiveness of the MaxEnt-FM model is illustrated by applying to real site investigation data.
Publisher
Springer Nature B.V
