Road modelling for infrastructure management
Bibliographic record
Abstract
The construction sector is undergoing a digital transition.Local authorities have adopted geographic information systems (GISs) to plan their territories and structure their services, such as transport.Simultaneously, building information modelling (BIM) has demonstrated its advantages during the design and construction phases of structures.An infrastructure project can rely on these two technologies to plan its implementation (GIS), to complete its design and construction (BIM), or to manage associated services, such as mobility (GIS).However, road maintenance, an important part of the infrastructure's life cycle, is not yet covered by these technologies.Road maintenance necessitates a comprehensive view of the infrastructure and its interactions with other realworld objects (e.g.vegetation, technical networks, or vehicles).Moreover, road managers are the local authorities that already use GISs.For these reasons, a GIS is suitable for fulfilling road maintenance requirements.This study proposes and applies a spatial framework (GIS) for road management, providing results on the organisation of a spatial road framework that is adaptable to the infrastructure's environment management.The spatial dimension must allow for the representation of the road and its components, including pavements and their dependencies.The structural dimension must be detailed to describe the layers, their formulations, and their thicknesses.The condition of the road must be described concisely so that the managers can plan maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".