Agile Frameworks in Construction Project Management: A Systematic Review
Bibliographic record
Abstract
The global production in the construction sector in 2020 was 10.7 trillion dollars and with a growth projection of 42% or 4.5 trillion dollars by 2030 to reach 15.2 trillion dollars, which will be a global engine for economic growth.For this reason, the present systematic review was based on the search and collection of exhaustive information; The main objective is to determine the state of the art of agile frameworks in construction project management over the past 15 years.Which consisted of searches of reliable database sources, where emphasis was placed on scientific articles in the civil construction sector using criteria of discarding and inclusion through the prism methodology for its filter, obtaining as a final result 37 articles between the years 2006 and 2023, One of the most important findings was the significant impact that agile frameworks have on improving efficiency and productivity in construction.By adapting to the dynamic and often unpredictable environment of construction projects, these frameworks allow for better resource management and faster, more effective decision-making.The results also highlight how implementing agile frameworks can help reduce costs and improve interpersonal and managerial skills, also known as "power skills."These benefits are clearly seen at all stages of the project, especially in the design phase, where flexibility and adaptability are critical.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".