BIM-based Automation for OTTV Calculation and Construction Cost Estimation in Energy-Efficient Building Design
Bibliographic record
Abstract
Due to the increasing trend of building energy consumption in Thailand, the Department of Alternative Energy Development and Efficiency (DEDE) established the Building Energy Code (BEC) to promote energy conservation.The BEC standard evaluates energy-efficient buildings with initial assessments conducted using the DEDE-developed software, BEC Version 1.0.6.The Overall Thermal Transfer Value (OTTV) is a crucial index in the BEC code for evaluating building energy efficiency.However, adding OTTV calculation task during the design stage may hinder the flow and time consuming.This study introduces a BIM-based tool developed using Dynamo, an add-in for Autodesk Revit, to assist designers in evaluating building designs while simultaneously controlling construction costs.The 3D model of eight alternative cases was created according to various building envelope and orientation settings.The instruction sets for OTTV calculation and construction cost of the wall extracted from the software were compared to the BEC program to validate its efficiency.The results demonstrate that the OTTV calculation using the developed instruction set closely aligns with the BEC program, achieving an R² value of 0.9898.Additionally, the instruction set for construction cost estimation produces lower costs compared to traditional methods.This workflow streamlines OTTV evaluation and construction cost estimation, enhancing design efficiency while ensuring compliance with energy standards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".