Engineering geological characterization of the 2014 Jure Nepal Landslide: An Integrated Field, Remote Sensing-Virtual/Mixed Reality Approach
Bibliographic record
Abstract
Characterization of unstable rock slopes can pose a high level of risk toward the geoscientist/engineer in the field due to inaccessibility and safety issues. During recent decades, rapidly developing remote sensing (RS) techniques, including Terrestrial Laser Scanning (TLS), Terrestrial Digital Photogrammetry (TDP), and Unmanned Aerial Vehicle Structure-from-Motion (UAV-SfM) are being progressively employed for landslide investigation and risk assessment. These methods allow acquisition of three-dimensional (3D) data sets from previously inaccessible terrain with sub-centimeter accuracy. This research describes an innovative approach to investigate the preliminary engineering geological characterization of a large (~5.5 Mm3), destructive landslide that occurred on August 2nd, 2014 near Jure in Sindhupalchok, ~70 km northeast of Kathmandu, Nepal. Various methods have been employed including traditional field surveys, RS techniques and preliminary 2D/3D numerical modelling with the objective of understanding conditioning factors, slope failure mechanisms, and identifying/mitigating future hazards at the site. With four years of RS data, analysis of strength degradation and progressive weakening of the rock mass is investigated by linking process of erosion and deposition using 3D change detection algorithms. The slope is still potentially in an unstable state, undergoing progressive rockfalls/slides with the most recent major event (~20,000 m3) in August 2017. Results throughout this thesis, including 2D/3D rock engineering mapping and modelling have been integrated into an interactive 3D Virtual/Mixed Reality (VR/MR) Jure Landslide geodatabase model, enabling an immersive and enhanced engineering 3D geovisualization experience. A comparative 2D/3D, and VR/MR rockfall simulations has been undertaken and developed within an augmented reality Microsoft HoloLens. Moreover, this thesis concludes on how VR/MR techniques can be employed to conduct discontinuity mapping on virtual outcrops and provide a game-changing way that geoscientists can communicate landslide investigation and risk assessment in all stages of rock engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".