Learning from the Past for a Sustainable Future: Environmental Monitoring and 3D Modelling to Assess the Thermal Performance of Heritage Buildings
Bibliographic record
Abstract
Abstract There are numerous lessons to be learned from historic buildings, such as the rich diversity of their traditional architecture, the use of natural and local materials, their durability and resilience, or because they allow for thermal comfort in severe climatic and weather conditions. Today, many of these heritage buildings are still standing and in use, but their shape may have changed significantly from when they were built. In this sense, to accurately analyse historic buildings, 3D models that approximate their geometry (as-is/as-built models) must be produced. Based on terrestrial laser scanning 3D point clouds, as-is 3D modelling can represent the geometrical alterations of the assets to enable diverse analyses and simulations. This work addresses Ye Olde Trip to Jerusalem building, claimed to be the oldest inn in England, UK (1189 AD). Hence, this historic building presents numerous deformations such as warped and tilted walls, recess in walls, non-planar ceilings, and an irregular arrangement of bent ceiling beams. This Grade II listed building is located near Nottingham Castle, beneath Castle Rock, the natural promontory on which the castle is situated. A part of the inn is inside rock-hewn caves under Castle Rock, making it a unique landmark with special indoor thermal conditions. Due to the complex geometry of the building, laser scanning-based 3D modelling is found essential to communicate the building’s features to help understand its thermal behaviour. This paper aims to investigate how Ye Olde Trip to Jerusalem building is capable of regulating indoor temperature and humidity in different locations, for which the as-is 3D modelling and environmental monitoring of this historic building are discussed. Based on the findings, the lessons learnt from studying old buildings could be utilised to enhance the sustainability of modern buildings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".