APPLYING BIM TO BUILT HERITAGE WITH COMPLEX SHAPES: THE ICE HOUSE OF FILARETE’S OSPEDALE MAGGIORE IN MILAN, ITALY
Bibliographic record
Abstract
Abstract. This paper presents the development of a BIM model for a stratified historic structure characterized by a complex geometry: Filarete’s Ospedale Maggiore ice house, one of the few remaining historic ice houses in Milan (Fig. 1). Filarete, a well-known Renaissance architect and theorist, planned the hospital in the 15th century, but the ice house was built two centuries later with a double-storey irregular octagonal brick structure, half under and half above ground, that enclosed another circular structure called the ice room. The purpose of the double-walled structure was to store ice in the middle and store and preserve perishable food and medicine at the outer side of the ice room. During World War II, major portions of the hospital and the above-ground section of the ice house was bombed and heavily damaged. Later, in 1962, the hospital was restored and rehabilitated into a university, with the plan to conceal the ice house’s remaining structure in the courtyard, which ultimately was excavated and incorporated into a new library for the university. A team of engineers, architects, and students from Politecnico di Milano and Carleton University conducted two heritage recording surveys in 2015 and 2016 to fully document the existing condition of the ice house, resulting in an inclusive laser scanner and photogrammetric point cloud dataset. The point cloud data was consolidated and imported into two leading parametric modelling software, Autodesk Revit© and Graphisoft ArchiCAD©, with the goal to develop two BIMs in parallel in order to study and compare the software BIM workflow, parametric capabilities, attributes to capture the complex geometry with high accuracy, and the duration for parametric modelling. The comparison study of the two software revealed their workflow limitations, leading to integration of the BIM generative process with other pure modelling software such as Rhinoceros©. The integrative BIM process led to the production of a comprehensive BIM model that documented related historic data and the existing physical state of the ice house, to be used as a baseline for preventive maintenance, monitoring, and future conservation projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".