HBIM CHALLENGE AMONG THE PARADIGM OF COMPLEXITY, TOOLS AND PRESERVATION: THE BASILICA DI COLLEMAGGIO 8 YEARS AFTER THE EARTHQUAKE (L’AQUILA)
Bibliographic record
Abstract
Abstract. In December 2012 ENIservizi (the Italian multi-national energy agency operating in many countries), after the Earthquake that occurred in April 2009, decided to undertake the project ‘Re-start from Collemaggio’ with the aim of giving new hope to the L’Aquila community, funding around 14 million Euro to restore the Basilica di Collemaggio. The Superintendence Office carried on the restoration project with the scientific support of the Università degli Studi de L’Aquila and the Università La Sapienza di Roma, under the coordination of the Politecnico di Milano. ENIservizi, aware of the BIM potential in the complex building and infrastructure domain in the world, required an advanced HBIM from the laser scanner and photogrammetric surveying to support the diagnostic analysis, the design project, the tender and the restoration itself, today still on course. Plans and vertical sections were delivered (2012) starting from the surveying campaigns (February and June 2013), together with the first HBIM advancement from the end of 2012 in support of the preliminary-definitive-executive steps of the restoration design project (2013-14-15). Five years later, this paper tries to make a synthesis of the different lessons learnt, in addition to the positive and critical aspects relating HBIM feasibility, sustainability and usefulness to the challenging restoration work. In particular, the Collemaggio BIM experience anticipated the new Italian Public Procurement Legislation (D.Lgs 50/2016, Nuovo Codice degli Appalti pubblici) aligned with to the EUPPD 24/2014: the EU Directive on Public Procurement asked all the 28 EU countries to adopt building informative modelling by February 2016 in order to support the whole LCM (Life Cycle Management), starting from the project and the intervention, through rewarding scores or mandatory regulations. Many analyses foresees to save from around 5% to 15% of the overall investment by adopting mature BIM (Level 3 to 5), particularly 4D remotely controlled BIM in support of the LCM, as in the case of maintenance and management process. The tender for Basilica restoration was published in 2015: the process was not developed enough to introduce selective criteria based on BIM adoption by the Construction Industry due to the lack of legislation at that time and the lack of BIM skills among the companies. Nevertheless ENIservizi also separately funded aside the HBIM of the Basilica to tackle an advanced BIM able to address decision-making processes in the heritage domain among the different actors: to support operators, architects, structural engineers, economic computation, construction site management and restoration, the theoretical and practical approach adopted by the HBIM, overcame the current logic based on sequential LoD (from simplex to complex, from the preliminary to the executive design) that is typical of new constructions in favour of a complex LoD approach that could guarantee management of the richness, unicity and multiplicity of each component and the maximum degree of knowledge in order to derive the decisions from the starting phases of the project. On the lesson learnt from this experience, the process of updating the current codification criteria (UNI11337-2009) was started with a draft proposal stimulating a debate for the future of HBIM adoption.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".