Reviving Heritage: A Comprehensive Fund-Allocation Decision Support System for Restoration of Historic Buildings
Bibliographic record
Abstract
Heritage buildings embody cultural identity and contribute significantly to economy and social well-being. However, many are severely deteriorated due to aging, neglect, and absence of effective management systems. Restoration decision-making is complex given the large number of structures in need for intervention, high restoration costs, limited funding, and the challenge of prioritizing interventions considering each building’s unique architectural, social, and economic value. This research, therefore, proposes a novel comprehensive computer-aided Decision Support System (DSS), inspired by infrastructure asset management system (IAMS), to optimize fund-allocation among heritage buildings under budget constraints. The DSS assesses the structures’ condition, vulnerability, uncertain deterioration behaviour, and intervention costs, alongside its unique value and expected socioeconomic benefit using multi-criteria decision-making methods. These inputs feed into mathematical optimization models that maximize structural performance and socioeconomic benefit over a defined funding period. To facilitate use by stakeholders, a user-friendly interface was developed. The system was applied to 39 severely deteriorated buildings in Historic Cairo, a UNESCO World Heritage Site, and was validated through discussions with key policymakers, confirming its practical value. In essence, this research offers a robust, data-driven tool for strategic restoration planning and sustainable heritage preservation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".