Improving Concrete Trade Labor Productivity through the Use of Innovations
Bibliographic record
Abstract
Concrete activities are typically critical to a project's schedule, therefore examining how to improve their labor productivity can have a direct impact on a project's overall performance. As part of a research program to improve construction productivity sponsored by the Construction Industry Institute (CII), the authors investigated innovations in the concrete trades and their impact on labor productivity. The innovations studied were 100ksi steel reinforcement, self-consolidating concrete (SCC), and modular formwork. The 100ksi reinforcing steel study analyzed a typical beam cross-section and compared its total weight to that of a typical 60ksi reinforcing design. Often, high strength reinforcing steel is a lower cost alternative to a standard design due to lower amounts of steel. The SCC study collected quantities and unit rates of SCC and a comparable conventional mix at several projects. The projects using SCC had faster placement unit rates compared to conventional concrete mixes. Modular formwork was found to have significant advantages in productivity over stick-built formwork systems. From the analysis of a sample project, modular formwork gains a cost advantage at varying floors based on local labor rates. The findings should help management understand performance of these concrete innovations when considering their use.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".