Recent progress and future outlook of digital twins in structural health monitoring of civil infrastructure
Bibliographic record
Abstract
Abstract Digital twins (DTs) have witnessed a paramount increase in applications in multidisciplinary engineering systems. With advancements in structural health monitoring (SHM) methods and implementations, DT-based maintenance and operation stages have been implemented significantly during the life cycle of civil infrastructure. Recent literature has started laying the building blocks for incorporating the concept of DTs with SHM of large-scale civil infrastructure. This paper undertakes a systematic literature review of studies on DT-related applications for SHM of civil structures. It classifies the articles based on thematic case studies: transportation infrastructure (i.e. bridges, tunnels, roads, and pavements), buildings, off-shore marine infrastructure and wind turbines, and other civil engineering systems. The proposed review is further uniquely sub-classified using diverse modeling approaches such as building information modeling, finite element modeling, 3D representation, and surrogate and hybrid modeling used in DT implementations. This paper is solely focused on applications relating DTs to SHM practices for various civil engineering infrastructures, hence highlighting its novelty over previous reviews. Gaps and limitations emerging from the systematic review are presented, followed by articulating future research directions and key conclusions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".