Restoration of Tilted Buildings via Micropile Underpinning: A Case Study of a Multistory Building Supported by a Raft Foundation
Bibliographic record
Abstract
This paper presents a real case study of a micropiling process that was developed to stop the continuous tilting of a 9-story residential building in Dakahlia, Egypt. Shortly after the construction of the building, the surface raft foundation exhibited severe settlement problems. In order to carry out a geotechnical investigation, boreholes were drilled around the constructed building. It was discovered that in addition to a thick, soft clay layer in the soil profile, there was also a crucial eccentricity between the centroid of the total building loads and the centroid of the raft. The issue needed to be addressed immediately, and a micropiling system was proposed to satisfy the geotechnical and structural conditions associated with the case history. In addition to describing the field measurements, detailed methodology, and micropile installation process, this paper also presents three different design approaches for determining the number and location of the micropiles. Although the underpinning process itself initially induced some settlement, micropiling the raft proved to be an efficient solution to stop the continuous tilting of the building. A micropile load test confirmed the advantageous effect of the grouting technique used for Type B micropiles, where the grout is injected under high pressure.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".