Digitalizing Bridge Management: Current Trends, Challenges, and a Practical Implementation Framework
Bibliographic record
Abstract
Transportation infrastructure, including highway bridges, are essential for both economic prosperity and societal well-being. Given the considerable number of bridges that each bridge manger is responsible for monitoring and the constraints on the availability of resources, it becomes impractical to continuously assess the integrity of each bridge using conventional approaches. Leveraging digitalization, driven by technologies such as bridge information modeling (BrIM) and digital twins, promises to significantly improve the productivity of the process, while ensuring bridge safety, durability, sustainability, and reduced life-cycle costs. Many stakeholders are enthusiastic about adopting digital technologies for bridge management but often lack a clear understanding of the benefits and how to realize them. This can lead to a focus on implementation rather than achieving defined objectives, which can hinder tangible improvements in practice. This paper provides clear definitions of relevant technologies and offers practical guidance to transform enthusiasm for digital technologies into actionable strategies for their effective implementation in bridge management practices. It proposes an incremental approach to digitalization in bridge management and conducts a thorough examination of the various levels of adoption. In addition, the paper identifies the opportunities and challenges of reaching full digitalization and highlights the transformative potential of digital solutions in enhancing bridge maintenance practices when implemented effectively.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".