Use of Real-Time Monitoring Data to Interpret Variable Subsurface Conditions and Validate Construction for a Large Deep Mixing Scheme Using Cutter Soil Mixing to Support MSE Walls at Kitimat LNG, BC, Canada
Bibliographic record
Abstract
The cutter soil mixing (CSM) technique was used to construct an extensive deep mixing scheme for foundation support of mechanically stabilized earth (MSE) walls at the Kitimat Liquefied Natural Gas (LNG) facility in Bish Cove, British Columbia, Canada. Using real-time monitoring from the Bauer B-tronic system, the performance of CSM in highly variable subsurface conditions that consisted of thick deposits of fine-grained soils overlying granular soils was assessed, and the data used to develop a set of termination criteria for deep mixing using the CSM technique that would achieve the ground improvement design requirements. Real-time monitoring data was also used for interpretation of highly variable subsurface conditions, identifying areas where modifications to the deep mixing design was required, allowing fine-tuning of the termination criteria used for problematic areas that included dense granular till, thick organic layers, and obstructions, and identifying areas where increased in-situ wet grab and/or post-installation core sampling and testing would be most appropriate for quality control (QC) and quality assurance (QA). The merits of using real-time monitoring data as part of the engineering decision-making process including the development of “inferred torque” based on pressures at the cutter wheels to develop a simplified termination criteria in highly variable soil conditions is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".