Curling of Concrete Floor Slabs on Grade — Causes and Repairs
Bibliographic record
Abstract
Many industrial floors are required to have high-quality flat surfaces for the operation of specialized equipment, particularly high-reach stackers operating from the surfaces of warehouse floors. For new floors, the essence of floor flatness lies in the manner of finishing and the systematic monitoring of the flatness achieved immediately following construction. Achieving such surfaces in floor slabs however, is quite difficult because of the moisture and temperature gradients that cause them to curl at the joints. Such curling seriously affects the operation of an industrial facility. Floors subjected to heavily loaded forklift traffic may rapidly deteriorate, causing safety problems. Curling is also exacerbated in industrial floors by the use of power-troweled surface hardeners to produce the dense high-strength top surface required for high wear resistance. Repair of curled floors in industrial locations involves grinding, patching, installation of dowels, and grouting underneath the curled slab. The timing and appropriateness of the method used are of vital importance to the durability of the repair. Aspects of design and construction to minimize curling of new industrial floors, the factors that contribute to cracking and curling, measures to minimize curling, and the repair of curled floors are discussed in this paper.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".