BIM-based energy consumption assessment of the on-site construction of building structural systems
Bibliographic record
Abstract
Purpose Steel and reinforced concrete are among the most common structural materials used in the construction industry. Cost and the speed of construction have been usually the main criteria when selecting a building’s structural system, whereby the environmental impact of the structural material is sometimes ignored. Availability of an easy-to-use tool for environmental assessment of the structural alternatives could encourage this evaluation in the decision making. The purpose of this paper is to introduce an automated tool for the environmental assessment of the on-site construction processes of a building structural system, which calculates the energy consumption and carbon emissions of the structural system as a parameter for comparison. Design/methodology/approach This assessment tool is implemented using a building information modeling (BIM) platform to extract structural elements and their key attributes, such as type, geometrical and locational data. These data are processed together with a productivity database to calculate machine hours, and then predefined energy and carbon inventories are used to assess the energy consumption of the structural system in the erection/installation stage. Findings This assessment tool provides an automated and easy-to-use approach to estimate energy consumption and carbon emissions of different structural systems that are modeled in a BIM platform. The results of this tool were within the ranges reported by the available studies. Originality/value This research project presents a novel approach to use BIM-based attributes of the structural elements to calculate the required efforts, i.e. machine hours, and assess their energy consumption and carbon emissions during construction processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".