Prediction of maintenance cost for road construction equipment: a case study
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
Equipment maintenance cost is significant in construction operations budgets. This study proposes a systematic approach to predict maintenance cost of road construction equipment. First, maintenance cost data over more than 10 years was collected from a partner company’s equipment management information system. Data was cleaned and analyzed to obtain a general understanding of maintenance costs trends. Next, traditional cumulative cost models and alternative data mining models were generated to predict maintenance cost based on available equipment and operation attributes. Data mining models were evaluated and validated using portions of the collected data that have not been used in model development. Data collection, analyses, modeling, and validation steps are discussed. The paper also presents the performance of different model types. Based on the case study data, regression model trees performed better than other model types with equipment work hours being the most significant parameter for predicting maintenance cost.
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 | 0.001 | 0.001 |
| 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