Investigating benefits and criticisms of BIM for construction scheduling in SMES: An Italian case study
Bibliographic record
Abstract
Scheduling is one of the driving factors in the success of a construction project and it is a critical step for all team members as it provides guidance to where and when they should perform work.Despite its important role, scheduling is typically based on approximate time schedules, which often lead to delays and extra costs.This is due to the lack of visualizing the real effort before the construction phase starts, as well as the difficulty in managing a large number of uncertain factors using traditional approaches.According to many studies, building information modeling aims at improving the quality of construction scheduling by enabling virtual simulations and by promoting digital working space.In this context, following questions arise: How does BIM support the digitalization of the scheduling process?Which are the benefits and criticisms of applying BIM for construction scheduling in a SME environment?This paper aims at evaluating the role of BIM in the digitalization of the construction scheduling process and its suitability for SMEs companies through a real case study.The paper starts with a state of the art on relevant applications of BIM for construction scheduling.Afterwards, the paper suggests a framework for combining construction scheduling and BIM, considering a case study used to verify the feasibility of the integrated method.Finally, findings from the implementation of the proposed framework are summarized, particularly examining the role of BIM in digitalizing the construction scheduling process, as well as the benefits and criticisms of its applicability to SMEs.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".