Industrializing Korean Traditional Housing (Hanok) with Hybrid Timber Construction
Bibliographic record
Abstract
In an attempt to disseminate traditional Korean culture, a comprehensive research has been initiated by Korean government in order to modernize Korean traditional housing (Hanok) with the objectives of improved energy performance and affordable construction cost. This äóÖHanokäó» project encompasses a wide spectrum of housing research including public policies, planning methods, standard design documents, new building materials and methods, construction standards, maintenance manuals, and advanced IT applications in an integrated manner. One of the biggest challenges in this äóÖHanokäó» project was to modernize the äóÖtraditional timber structureäó» for industrialization, while keeping the traditional way of aesthetic representation. As a solution to meet this complicated requirement, a hybrid timber system was developed by combining traditional methods and industrialized modular members. Different timbers and methods are used together for different part of house elements resulting in cost reduction by 50% for the timber frame. Major criteria for applying different methods include the aesthetic representation, economy, and deformation behaviour of wood. Automated computer numeric control (CNC) machine, standard 3D-CAD objects, standard classifications, and computer applications were also developed in order to make this hybrid system economically feasible. This paper introduces the hybrid timber system for Hanok along with supportive application systems for industrialization. Three mock-up projects, actually built as part of this research project, are compared and analysed in order to illustrate how the proposed hybrid timber system has evolved during the research and development. Lessons learned and future directions will be also briefly discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".