Safety Assessment of the Impacts of Foundation Pit Construction in Metro Station on Nearby Buildings
Bibliographic record
Abstract
During the construction of metro station, it is important to ensure the safety and normal use of nearby buildings.Focusing on a foundation pit project in Hefei Metro, this paper fully examines the foundation type, structural form, construction period and use state of a nearby building, and explores the relationship between foundation pit support plan, metro station construction plan, and the residual deformation capacity of the building foundation.Based on the design files and different construction plans of the station, the finite-difference method and deformation observation were adopted to analyze the foundation settlement and residual deformation of the nearby building during metro station construction.The support plan and construction plan were adjusted and improved continuously.Finally, it is planned to support the foundation pit with reinforced concrete retaining piles and four-layer steel supports, and excavate and reinforce the pit layer by layer.Under the final plans, deformation of the building foundation does not exceed its remaining deformation capacity.This research provides an effective construction plan for the metro station, and specifies the way to observe the foundation settlement of the nearby building.The research results provide a good reference for similar risk projects to conduct safety assessment and evaluation in the design and construction phases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".