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Probabilistic prediction of intact rock strength using point load tests using a Bayesian formulation
This paper develops probabilistic predictive models for the unconfined compressive strength of intact rock based on the point load strength tests. A database of point load strength and unconfined compressive strength measurements for sedimentary, igneous and metamorphic rocks is first developed. The measurements are corrected to their standard equivalent values, namely unconfined compressive strength and point load strength for specimens with 50 mm diameter. A Bayesian framework is used to assess different candidate model forms and to estimate the corresponding model parameters. This Bayesian approach uses the prior information for the values of the unconfined compressive strength and point load strength measurements to develop an unbiased method for prediction of unconfined compressive strength based on point load strength tests. The Bayesian estimation of the model parameters accounts for the relevant sources of uncertainty including aleatory and epistemic uncertainties and for possible measurement errors in the point load strength test data. Probabilistic models are developed for sedimentary, igneous and metamorphic rocks. It is found that (i) a model of form provides the best fit to the laboratory data, and (ii) accounting for measurement errors and separating the database into subgroups based on different rock types decrease the model uncertainty.
Probabilistic prediction of intact rock strength using point load tests using a Bayesian formulation
This paper develops probabilistic predictive models for the unconfined compressive strength of intact rock based on the point load strength tests. A database of point load strength and unconfined compressive strength measurements for sedimentary, igneous and metamorphic rocks is first developed. The measurements are corrected to their standard equivalent values, namely unconfined compressive strength and point load strength for specimens with 50 mm diameter. A Bayesian framework is used to assess different candidate model forms and to estimate the corresponding model parameters. This Bayesian approach uses the prior information for the values of the unconfined compressive strength and point load strength measurements to develop an unbiased method for prediction of unconfined compressive strength based on point load strength tests. The Bayesian estimation of the model parameters accounts for the relevant sources of uncertainty including aleatory and epistemic uncertainties and for possible measurement errors in the point load strength test data. Probabilistic models are developed for sedimentary, igneous and metamorphic rocks. It is found that (i) a model of form provides the best fit to the laboratory data, and (ii) accounting for measurement errors and separating the database into subgroups based on different rock types decrease the model uncertainty.
Probabilistic prediction of intact rock strength using point load tests using a Bayesian formulation
Guevara-Lopez, F. (author) / Jimenez, R. (author) / Gardoni, P. (author) / Asem, P. (author)
2020-07-02
10 pages
Article (Journal)
Electronic Resource
Unknown
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