Advancing civil infrastructure with digital twins: a review of applications and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The digital twins (DTs) technology has emerged as a ground-breaking approach in the management and maintenance of civil infrastructure, providing a virtual representation of physical systems which are continuously updated with realtime data from IoT sensors and simulations. Initially introduced in the manufacturing sector, the concept of digital twins has been extended to civil engineering, offering a significant potential for real-time monitoring, predictive maintenance, optimized asset management, and enhanced decision-making. This paper provides a comprehensive survey of the applications of the digital twins technology in civil infrastructure, with a particular focus on structural health monitoring (SHM), predictive maintenance, smart city frameworks, and disaster response systems. By reviewing existing methodologies, case studies, and practical implementations, this paper highlights the transformative impact of DTs in improving the efficiency, safety, and sustainability of infrastructure systems, including bridges, buildings, and transportation networks. Despite the numerous advantages of DTs, several challenges impede their widespread adoption in civil engineering. These challenges include high implementation costs due to the need for sophisticated sensors, high-performance computing, and advanced simulation tools. Additionally, data integration and interoperability issues between various data sources and platforms hinder seamless adoption. Cybersecurity risks associated with real-time monitoring systems and the protection of critical infrastructure are also discussed. This survey identifies these barriers and outlines the necessary technological advancements which may help overcoming the barriers. These include standardized data formats, enhanced AI-driven predictive models, and scalable cloud solutions, among others. This paper concludes by highlighting future research directions to address the identified challenges, emphasizing the need for collaboration across academia, industry, and government to fully unlock the potential of DTs technology. With continued advancements in machine learning, edge computing, and secure data protocols, DTs are poised to revolutionize infrastructure management, contributing to smarter, safer, and more efficiently built environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it