Hybrid feature selection framework for predicting bridge deck conditions
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
Bridge decks’ maintenance funding requirements are influenced by bridge decks' current and predicted future conditions. Additionally, the serviceability of bridges may be negatively impacted by the degradation of bridge decks. Bridge inspections require considerable effort, time, cost, and resources; besides, such inspections may introduce hazards and safety concerns. This paper introduces a data-driven hybrid feature selection framework for predicting bridge deck deterioration conditions and applying it to a bridge deck in Iowa State, USA. Firstly, the Boruta algorithm, stepwise regression, and multi-layer perceptron are employed to find the best subset of features that contribute to bridge deck deterioration. Then, four classification models were developed using the best feature subset of features, namely k-nearest neighbours, random forest, artificial neural networks, and deep neural networks. The hyperparameters of the models were optimized to get their best performance. The developed models showed comparable performance, and the random forest model outperformed the other models in prediction accuracy with fewer misclassifications. The developed models are thought to reduce field inspections and give insights into the most influential factors in bridge deck deterioration conditions.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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