Rock cuts with presence of water has significant mass change under the freeze-thaw cycle. Thermal images provides another dimension to analyze the thermal effects, and pre-planed drone flight affordable monitoring in long term and large scale. The reconstructed 3D model serves as a reliable carrier for further mechanical analysis and mitigation design, known as the digital twin.
Challenges:
* There is a tourist captured in the scene, can you find it?
* Given brighter object are hotter, why are there some rock layer lit up while they are all shaded?

Increasing temperatures and extreme weather events driven by climate change have heightened the risk of rockfall hazards. Reliable estimates of rockfall hazards have a significant impact on the design of roadways, slopes, open pits, and underground excavations. The reliability of these estimates relies on numerical models calibrated using rockfall catalogs. Recent advancements in radar technology enable high-resolution monitoring of rockfall events, providing valuable data on their propagation. However, there are few methods to back-analyze numerical model parameters from radar data. Hence, we propose a Bayesian Optimization Framework to address this gap and demonstrate the framework through a Doppler-radar-tracked rockfall catalog comprising 21 events and 19,356 rock positions. Specifically, we use Gaussian Process Regression (GPR) as a surrogate model to minimize the number of simulations required to determine the best-fitting coefficients of restitution (CORs). The framework supports event-based and site-wise analysis. The former can readily identify unmatchable low-quality events, and the latter excels in fitting CORs generalizable across multiple events. Our framework achieves comparable resolution using five times fewer simulations compared to a benchmark grid-search method. Trajectories simulated using the mean and covariance of the best-fit CORs exhibit reasonable agreement with the measured data. This framework accelerates data inspection and parameter searching when back-analyzing numerical model parameters using a catalog of multiple rockfall events. (Up to 20x faster!)

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 R_n = 0.866 ± 0.109 and tangential COR R_t = 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 R_n to 0.8 ± 0.014 and R_t to 0.39 ± 0.065. Both scenarios show a higher uncertainty in R_t than in R_n. 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).

Rock fracture mechanisms can be inferred from moment tensors (MT) inverted from microseismic events. However, MT can only be inverted for events whose waveforms are acquired across a network of sensors. This is limiting for underground mines where the microseismic stations often lack azimuthal coverage. Thus, there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network. Here, we present a novel, multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform. The framework consists of a deep learning model that is initially trained on 2400000+ manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations. Transfer learning is then applied to fine-tune the model on 300000+ MT-labelled lab-scale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts, loading, and rock types in training. The optimal model achieves over 86% F-score on unseen waveforms at both the lab- and field-scale. This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network. This facilitates rapid assessment of, and early warning against, various rock engineering hazard such as induced earthquakes and rock bursts.

1) AI-driven Quantitative Disc Cutter Wear prediction

https://doi.org/10.1016/j.tust.2022.104654

2) Optimization:
Hyperparameters tuning with
Bayesian Optimization (SMBO) and Genetic Algorithm

DOI: 10.1201/9781003348030-316

3) Equation derivation:
Face stability of shield tunneling in rock-soil interface composite formation

https://doi.org/10.1016/j.undsp.2022.01.007

4) Rubrics for Quantitative Tunneling Project Management

DOI: 10.3390/buildings13020355