The Dry Construction Systems on the Rehabilitation of Built Heritage
Bibliographic record
Abstract
The dry construction techniques, widely used in past centuries have seen a renewed interest in the last few years. This is due to different reasons such as the new user’s needs for high quality at low cost, the shortage of traditional skilled labor, the need to reduce delivery times and the rising costs of initiating a fabrication plant. Dry construction methods regard the building site as the place of assembly. The quality of the finish products, are guaranteed by a factory controlled production process and reduction to a minimum of on-site work. The building, designed by “unconnected boxes” becomes an “active machine”, capable of ensuring maximum performance for the user. Finally the design of an “open building system” also consists of a set of rules to allow creation of various solutions. The complexity of this modus operandi increases progressively if the intervention is carried out in small historic centres. Therefore, this research aims at presenting a method of work that uses dry construction systems and that has been developed to intervene in the historic contexts damaged by the earthquake that struck the Abruzzo region on April 6, 2009. This method develops a process that aimes at the rehabilitation of the buildings but also at improving their energy behavior while respecting, at the same time, the vernacular values. It is based on a “case by case” approach that starts from an analysis of the context and its local construction techniques, taking into account the peculiarities of each location. The results of this method have been applied to a small village located in the province of L'Aquila called Santa Maria del Ponte.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".