A State-of-the-Art Review and Bibliometric Analysis on the Smart Preservation of Heritages
Bibliographic record
Abstract
The preservation of heritage buildings is a crucial endeavour for countries worldwide. This study presents a comprehensive bibliometric analysis of the latest trends in smart applications for heritage building preservation within the context of Industry 4.0 and Industry 5.0, covering the period of 2020–2024. A total of 216 peer-reviewed journal articles obtained from the Scopus database were subjected to analysis using RStudio and VOSviewer. The methodology was based on a dual analysis, including surface-level examination and in-depth exploration. Consequently, a new conceptual framework is presented for achieving smart preservation of heritages. It is structured based on two pillars: the physical methods pillar, including smart devices and smart processes, and the digital methods pillar, involving smart technologies and environments. Also, the results revealed that the dominant portion of literature publications (61%) emphasize specific topics such as interoperability, monitoring, data management, and documentation. However, training and community engagement represent an insufficient fraction (2–6%), and more research is needed in the future. This paper concludes by discussing a future innovative vision for policy and industry through urging policymakers to promote interoperability standards; address data security; and fund innovative, low-cost technologies, as well as advocating the industry sectors for public engagement, sustainable preservation, and prioritizing skill development programs and workforce.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.052 | 0.065 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".