当前位置: X-MOL 学术Int. J. Rock Mech. Min. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Bayesian analysis for uncertainty quantification of in situ stress data
International Journal of Rock Mechanics and Mining Sciences ( IF 7.0 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.ijrmms.2020.104381
Yu Feng , Nezam Bozorgzadeh , John P. Harrison

Abstract Estimates of in situ stress state may be unreliable due to the inherent variation of stresses in rock masses coupled with the usual lack of sufficient stress data. This renders uncertainty quantification critically important for stress estimation, as it both permits quantitative assessment of the reliability of estimated stresses and facilitates application of reliability-based design in rock engineering. This paper presents a Bayesian approach that can probabilistically quantify uncertainty in both mean stress estimation and predicted stresses. We show that the quantified uncertainty supports our general understanding in that (i) more stress data tend to result in more reliable estimates, and (ii) the usual case of small numbers of stress data is liable to yield highly uncertain estimates of the mean stress. The results reveal that large uncertainty may exist in estimates of both the magnitudes and orientations of the principal mean stress, and this suggests that in practice the estimation reliability should be considered for not only stress magnitudes but also stress orientations. The results also show that the three principal mean stress components may display different degrees of uncertainty, thereby highlighting the importance of identifying which mean stress components are of most interest for the specific engineering objective. Finally, we demonstrate how, within the Bayesian framework, the large uncertainty in mean stress estimates arising from limited stress data can be effectively reduced by means of informative priors.

中文翻译:

用于原位应力数据不确定性量化的贝叶斯分析

摘要 由于岩体中应力的固有变化以及通常缺乏足够的应力数据,对地应力状态的估计可能不可靠。这使得不确定性量化对于应力估计至关重要,因为它既允许对估计应力的可靠性进行定量评估,又促进了基于可靠性的设计在岩石工程中的应用。本文提出了一种贝叶斯方法,可以概率性地量化平均应力估计和预测应力的不确定性。我们表明量化的不确定性支持我们的一般理解,因为 (i) 更多的压力数据往往会导致更可靠的估计,以及 (ii) 少量压力数据的通常情况可能会产生对平均压力的高度不确定的估计. 结果表明,主平均应力的大小和方向的估计可能存在很大的不确定性,这表明在实践中不仅应考虑应力大小而且应考虑应力方向的估计可靠性。结果还表明,三个主要平均应力分量可能表现出不同程度的不确定性,从而突出了确定哪些平均应力分量对特定工程目标最感兴趣的重要性。最后,我们展示了如何在贝叶斯框架内通过信息先验有效减少由有限压力数据引起的平均压力估计的巨大不确定性。这表明在实践中,不仅应考虑应力大小而且还应考虑应力方向的估计可靠性。结果还表明,三个主要平均应力分量可能表现出不同程度的不确定性,从而突出了确定哪些平均应力分量对特定工程目标最感兴趣的重要性。最后,我们展示了如何在贝叶斯框架内通过信息先验有效减少由有限压力数据引起的平均压力估计的巨大不确定性。这表明在实践中,不仅应考虑应力大小而且还应考虑应力方向的估计可靠性。结果还表明,三个主要平均应力分量可能表现出不同程度的不确定性,从而突出了确定哪些平均应力分量对特定工程目标最感兴趣的重要性。最后,我们展示了如何在贝叶斯框架内通过信息先验有效减少由有限压力数据引起的平均压力估计的巨大不确定性。从而突出了确定哪些平均应力分量对特定工程目标最感兴趣的重要性。最后,我们展示了如何在贝叶斯框架内通过信息先验有效地减少由有限压力数据引起的平均压力估计的巨大不确定性。从而强调了确定哪些平均应力分量对特定工程目标最感兴趣的重要性。最后,我们展示了如何在贝叶斯框架内通过信息先验有效地减少由有限压力数据引起的平均压力估计的巨大不确定性。
更新日期:2020-10-01
down
wechat
bug