Improving Construction Environmental Metrics through Integration of Discrete Event Simulation and Life Cycle Analysis
Bibliographic record
Abstract
Improving Construction Environmental Metrics through Integration of Discrete Event Simulation and Life Cycle Analysis H. Golzarpoor, V. González, M. Poshdar Pages 130-139 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Life Cycle Analysis (LCA) is a methodology for evaluating the environmental impacts associated with a product during its life cycle. LCA is identified as the most reliable method for verifying environmental impacts; however, current LCA-based approaches have certain limitations for environmental analysis of construction products. Integration of the LCA methodology with Discrete Event Simulation (DES) provides a sound framework for modeling and analysing the environmental impacts of construction products. LCA and DES is one possible combination for analysing the cause and effect of various scenarios where time, resources, and randomness of input variables affect the outcome and, therefore, has the potential to address the shortcomings of LCA in construction. Recent studies in disciplines other than construction such as manufacturing systems have revealed positive effects on evaluation of environmental metrics while integrating LCA with DES; however, this integration has not yet been applied for environmental analysis of construction products. By implementing LCA data in a DES model, this research proposes an environmental model of earthmoving operations in a case study. Environmental variables are simultaneously assessed with production variables in the same simulation model and the integration of DES and LCA is discussed. Keywords: Discrete Event Simulation, Life Cycle Analysis, Environmental Analysis, Construction Management DOI: https://doi.org/10.22260/ISARC2013/0014 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".