Exploring the synergies between Life Cycle cost / Whole Life Cost and Building Information Modeling: A Systematic Literature Review
Bibliographic record
Abstract
Abstract Life Cycle Costing (LCC) is a cost estimating approach for project and asset planning and delivery that considers the direct and indirect costs incurred over the entire life cycle of an asset. This approach can be expanded to the concept of Whole Life Cost (WLC), which additionally considers externalities and benefits. WLC can demonstrate the financial impacts, both positive and negative, of a project on its environment, in other words it can show its complete value. Despite its potential, the approach is still perceived as complex because, among other things, access to data can be difficult and the approach is still not supported by a standardized methodology. Building Information Modeling (BIM) could be used to address these issues as both WLC and BIM are deemed complementary. BIM provides WLC with better data management, improved calculation accuracy and visualization of project impacts. In return, WLC improves project understanding, decision making and reinforces life cycle thinking. This paper aims to study the potential synergies between BIM and WLC through a systematic literature review. The identification of these synergies helped form a frame of reference to better understand the opportunities that this combination can offer. Future studies would be needed to explore the application of BIM and WLC at different project scales and identify the context in which the combination of BIM and WLC is the most beneficial.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".