Scalable probabilistic deterioration model based on visual inspections and structural attributes from large networks of bridges
Bibliographic record
Abstract
Visual inspections of large networks of bridges yield millions of data points scattered across thousands of structural elements. Alongside visual inspections, structural attributes such as age, location and traffic load provide contextual information about the deterioration patterns in the network. Leveraging this network-scale data for modeling deterioration is challenging, especially when each structural element has few inspections over a long period of time. Moreover, as new bridge information and inspections are added each year, it is strictly important for deterioration models to be scalable. This paper addresses these challenges by proposing a scalable probabilistic approach for modeling deterioration of large networks of bridges. The new framework consists of state-space models (SSM) for modeling the deterioration based on visual inspections and a Bayesian neural network (BNN) that factors-in information about structural attributes. The role of the BNN model is to learn the mapping between the initial distribution of the deterioration speed and the structural attributes of each bridge. The new framework is shown to be computationally efficient and can seamlessly incorporate a large number of structural attributes, which alleviates the need for feature selection. In addition, the proposed framework incorporates a new approach for learning the inspectors’ uncertainty parameters which is shown to provide better generalization. The experiments in this study are based on real data from the network of bridges in the province of Quebec, Canada. • Scalable probabilistic deterioration model for large networks of bridges. • Deterioration modeling is based on visual inspections while considering structural attributes. • A new approach for quantifying the uncertainty associated with each inspector. • Validation analyses on inspection data from the network of bridges in the Quebec province.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".