Semi-Empirical Method for Excavation-Induced Surface Displacements—Los Angeles Metro K Line Crenshaw/LAX Transit Project
Bibliographic record
Abstract
Estimating excavation-induced ground surface displacements in urban areas is needed to assess potential structure damage. Empirical settlement distribution models have been widely used to estimate the zone of influence and ground response behind braced excavation walls. Three underground station excavations, part of the Los Angeles Metro’s K Line Crenshaw/LAX Transit Project, offer a unique opportunity to collect field instrumentation data to improve estimates of ground deformations. One excavation employed cross-lot braces and soldier piles and wood lagging while the other two were supported by cross-lot braces and stiffer Cutter-Soil-Mixing (CSM) walls. For the excavations with stiff support systems and relatively small wall movements, upward surface displacement or heave governed the ground surface response, while surface settlement was measured at the excavation with the more flexible wall system. This heave behavior is often masked by settlement caused by relatively large wall movements, and is thus commonly disregarded. By idealizing the excavation unloading as an upward strip load at the ground surface, the Boussinesq solution for elastic upward movement can be used in combination with a settlement component resulting from lateral wall movements to estimate the magnitude and distribution of excavation-induced surface displacements.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".