WEATHER-RELATED GEO-HAZARD ASSESSMENT MODEL FOR RAILWAY EMBANKMENT STABILITY
Bibliographic record
Abstract
The primary objective of this thesis is to develop a model for quantification of weather-related railway embankments hazards.The model for quantification of embankment hazards constitutes an essential component of a decision support system that is required for the management of railway embankment hazards.A model for the deterministic and probabilistic assessment of weather-related geo-hazards (W-GHA model) is proposed based on concepts of unsaturated soil mechanics and hydrology.The model combines a system of two-dimensional partial differential equations governing the thermo-hydro-mechanical behaviour of saturated/unsaturated soils and soil-atmosphere coupling equations.A Dynamic Programming algorithm for slope stability analysis (Safe-DP) was developed and incorporated into the W-GHA model.Finally, an efficient probabilistic and sensitivity analysis framework based on an alternative point estimate method was proposed.According to the W-GHA model framework, railway embankment hazards are assessed based on factors of safety and probabilities of failures computed using soil property variability and case scenarios.A comprehensive study of unsaturated property variability is presented.A methodology for the characterisation and assessment of unsaturated soil property variability is proposed.Appropriate fitting equations and parameter were selected.Probability density functions adequate for representing the unsaturated soil parameters studied were determined.Typical central tendency measures, variability measures, and correlation coefficients were established for the unsaturated soil parameters.The inherent variability of the unsaturated soil properties can be addressed using the probabilistic analysis framework proposed herein.A large number of hypothetical railway embankments were analysed using the proposed model.The embankment analyses were undertaken in order to demonstrate the application of the proposed model and in order to determine the sensitivity of the factor of safety to the uncertainty in several input variables.The conclusions drawn from the sensitivity analysis study resulted in important simplifications of the W-GHA model.It was shown how unsaturated soil mechanics can be applied for the assessment of near ground surface stability hazards.The approach proposed in this thesis forms a protocol for application of unsaturated soil mechanics into geotechnical engineering practice.This protocol is based on predicted unsaturated soil properties and based on the use of case scenarios for addressing soil property uncertainty.Other classes of unsaturated soil problems will benefit from the protocol presented in this thesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".