Impact of Errors and Omissions in Concrete Slab Penetrations in Construction Projects
Bibliographic record
Abstract
Concrete slabs are essential elements in various building structures, acting as floors, foundations, and load-bearing components.However, Errors and omissions in slab construction can lead to serious consequences, such as safety risks, project delays, and higher costs.The problem this research paper addresses is the impact on construction sites due to the errors and omissions in concrete slab rough-in of Mechanical, Electrical, and Plumbing (MEP) systems.Thus, this paper aims to quantify the impacts of MEP System errors and omissions in concrete slabs such as incorrect dimension, wrong position, inappropriate securing of elements, and complete omissions of items.The methodology employed in this paper was quantitative descriptive.Because quantitative methodology guided the systematical collection and analysis of numerical data and descriptive methodology allowed the research team to capture a snapshot of the current state of error and omissions.The data collection instrument was an online survey, and the analysis was done through descriptive statistics.The results show that 62.5% of responses reported project delays, 93.8% experienced increased costs, and 15.4% reported a very high or high safety impact.The intellectual merit of this work is that it addresses the knowledge gap on the impact of errors and omissions in MEP rough-ins within concrete slabs
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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 | 0.012 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".