| Autor: |
Xie, Arnold Yuxuan, Huang, Zhanyu, Yacoub, Thamer, Li, Bing Q. |
| Zdroj: |
Journal of Rock Mechanics and Geotechnical Engineering; 20240101, Issue: Preprints |
| Abstrakt: |
Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards. Nowadays, Doppler radar technologies can measure rockfall trajectories with centimeter resolution. Calibrating a numerical model to fit these measured trajectories, i.e. back analysis, often involves manual trial-and-error processes and subjective goodness-of-fit criteria. Here, we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories. The framework can also quantify the uncertainty bounds on the best-fit model parameters. The approach is validated using field data from an Australian copper mine under two scenarios. (1) We perform an unconstrained back-analysis where the initial position and velocity of the rock, in addition to the coefficients of restitution (COR), are free variables. This scenario yields a normal COR Rn = 0.866 ± 0.109 and tangential COR Rt = 0.29 ± 0.151 with 68% confidence. (2) We perform a constrained back-analysis using predetermined initial position and velocity of the rock, which further constrains Rnto 0.8 ± 0.014 and Rtto 0.39 ± 0.065. Both scenarios show a higher uncertainty in Rtthan in Rn. We also demonstrate the adaptability of the back-analysis framework to two-dimensional (2D) rockfall modeling using the same data. To the best of our knowledge, this is the first quantitative goodness-of-fit metric for trajectory-based rockfall back analysis that supports the estimation of inherent uncertainty. The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions (e.g. rigid-body mechanics) and metrics (e.g. velocity or energy). |
| Databáze: |
Supplemental Index |
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