Diaphragm wall lateral movement in deep excavations in Bangkok clays: impacts and influencing factors
Bibliographic record
Abstract
Deep excavation in Bangkok clay layers involving diaphragm walls can cause ground movement, potentially affecting nearby structures. Understanding the magnitude and profile of this movement is crucial for assessing its impact on adjacent buildings. This study examines factors influencing the lateral displacement of rigid diaphragm walls in Bangkok’s deep excavations, including construction methods, excavation duration, depth ratios, soft clay depth, and system stiffness. The research data were collected from 230 dataset of lateral movement in diaphragm walls with a thickness ranging from 0.60 to 1.50 m. These walls had toe depths between 14 and 65 m, across various excavation depths ( H e ) from 6 to 35 m. Maximum lateral wall displacements ranged from 0.10% H e to 0.27% H e for the top-down method, and from 0.20% H e to 0.50% H e for the bottom-up method. If the system stiffness is sufficient, variations in wall thickness and construction method have minimal impact on wall deflection. However, with the bottom-up method and 1.0 m thick walls, long excavation times can lead to displacements up to 0.60% H e . This is mainly due to consolidation and creep in the clay beneath the area where the base slab construction is delayed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".