Potential Integration of Bridge Information Modeling and Life Cycle Assessment/Life Cycle Costing Tools for Infrastructure Projects within Construction 4.0: A Review
Bibliographic record
Abstract
Construction 4.0 is a platform that combines digital and physical technologies to enhance the design and construction of the built environment. Bridge Information Modeling (BrIM), a component of Construction 4.0′s digital technologies, streamlines construction processes and promotes collaboration among project stakeholders. In this study, a comprehensive literature review and bibliometric and content analysis are conducted on building information modeling (BIM), life cycle assessment (LCA), life cycle cost (LCC), BrIM, and Bridge LCA. This study investigates the potential integration of BrIM, LCA, and LCC as inputs for bridges’ LCA to enhance decision making by providing designers with detailed and interactive cost and environmental information throughout an asset’s lifecycle and explores the functionalities of Construction 4.0 and its potential influence on the economy and sustainability of bridge projects. The reviewed literature showed that the tools currently used to apply LCA and LCC methods for infrastructure assets lack the ability to identify possible integration with BrIM and hold limitations in their key functions for identifying the utmost features that need to be adopted in the creation of any tool to increase the general resilience of bridges and infrastructure.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".