A comparison of concrete quantities for highway bridge projects: preconstruction estimates vs onsite records
Bibliographic record
Abstract
This paper compares onsite concrete quantities to preconstruction estimates for 18 highway bridges in Canada to quantify the differences in quantities and to identify the driving factors. Material estimates completed during planning and design play a crucial role in predicting project cost, duration, and embodied CO2e emissions for construction projects. However, there is limited understanding of estimating material quantities for construction projects, and their impacts on other estimating processes, e.g., project cost, project schedule, embodied CO2e assessments. Results show that 3–87% greater concrete quantities are used onsite compared to estimates, with the bridges’ substructures responsible for most of the discrepancy. The findings of this study inform our understanding of the preconstruction estimates and their interpretation. Adjusting for the discrepancy between estimates and onsite measurements as well as targeting the drivers of unexpected material use has the potential to reduce environmental impacts, minimize cost overruns, and limit project delays.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".