The Role of Digital Twins in Construction Project Lifecycle Management: Enhancing Efficiency and Innovation in the United States
Bibliographic record
Abstract
Digital Twin (DT) technology use throughout the lifecycle of infrastructure projects is revolutionizing infrastructure planning, construction, and operation in the United States. The paper discusses systematically 138 publications (2016–2024) to analyse DT applications, advantages, and disadvantages in every phase of the lifecycle: design and planning, construction and execution, operation and maintenance, and decommissioning. The methodology is based on the PRISMA protocol and consists of scient metric analysis and thematic categorization. Adoption of DT is most developed in the design and maintenance stages, where it is combined with BIM, AI, and IoT to optimize efficiency, predictive analytics, and sustainability performance. Empirical research on large-scale U.S. projects like the Los Angeles Metro extension and Orlando Smart City indicates enhanced project visualization, cost management, and operational resilience. Nonetheless, lifecycle implementation at scale is constrained by data interoperability, cybersecurity, and standardization deficiencies. DTs are considered a driver of digital transformation for American construction, facilitating collaboration, minimizing rework, and advancing national sustainability goals under the Infrastructure Investment and Jobs Act. Results offer a strategic plan for leveraging DT technology to maximize project efficiency, lifecycle performance, and innovation in the U.S. built environment.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".