Model-Based Recommendation System In Support Of Construction Equipment Management - A Case Study
Bibliographic record
Abstract
Major contractors are relying increasingly on computerized equipment management systems to accomplish their objectives in construction equipment management, including maintenance, repair, operations and other managerial tasks. Though the automatic or semi-automatic collection of electronic data for equipment management has been made possible via on-board controls, handheld devices, and electronic fuel data transfer, the output of most current management systems are still delivered through reports that represent facts “as-it-is” and lack a mechanism for automatic knowledge discovery and utilization of the collected data. By means of a case study on real-time work order evaluation, this paper presents our research on embedding data mining modules into current equipment management information systems so that the hidden patterns in the data can be explicitly discovered and represented to the user. This paper will also demonstrate how new cases are predicted using the trained predictive model. The proposed approach is capable of making real-time predictions based on facts rather than experiences; in addition, it explains the logic of reasoning using the transparent data mining models.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".