Analysis of the Applicability of the BIM Methodology to the Management of Municipal Public Infrastructure
Bibliographic record
Abstract
This study investigates the applicability of the Building Information Modeling (BIM) methodology to the management of public infrastructure projects, focusing on medium-sized municipalities. To achieve this, its methodological approach consists of a systematic review of international and national literature, combined with a case study of a city hall in the Southern Agreste region of Pernambuco. The systematic review highlights that BIM offers advantages such as interoperability, three-dimensional visualization, integration with emerging technologies, support for predictive maintenance, sustainability, and increased efficiency. It also identifies the main factors that are critical to implementation in public institutions, such as a lack of technical capacity, high initial costs, institutional resistance, lack of specific regulations, and difficulties in interoperability with legacy systems. The case study, in turn, reveals that the city hall under analysis has a low degree of digital maturity, a lack of technical knowledge about BIM, and budgetary constraints, despite demonstrating interest and institutional openness to its adoption. The practical application of the model makes it possible to map the current stage of BIM adoption, identify local barriers, and propose guidelines adapted to the municipal context, such as training schedules, stages of technical collection digitization, and strategies for public procurement that require BIM models. The results indicate that, even in the face of constraints, it is possible to develop a progressive plan for implementing the methodology, provided that there is institutional support, clear public policies, and investment in training. It is concluded that BIM can significantly contribute to the modernization of public infrastructure management, provided it is contextualized to local limitations and potential, and it is recommended as a strategic tool for strengthening municipal governance and improving the quality of public works.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".