Data warehousing for construction equipment management
Bibliographic record
Abstract
Equipment logistics, maintenance, and repair are important aspects of construction equipment management. A well-managed equipment fleet helps reduce downtime, as well as total maintenance and repair costs. With quickly growing fleets of equipment, large contractors tend to divert the maintenance and repair of equipment from equipment managers to project managers. As a result, the equipment managers shift their attention from operational-level decision-making to corporate-level strategic decision-making regarding equipment management, which is often a challenging job with the current equipment management system. This paper presents an equipment data warehouse and a prototype decision support system (DSS). The proposed equipment data warehouse enables equipment managers to visually analyze the equipment fleet data from different perspectives and at various level of details. The data-warehouse-based DSS facilitates high-level, fact-based decision-making regarding equipment logistics, supplies, maintenance, repair, and replacement and has higher levels of performance and flexibility than the current equipment management system.Key words: data warehouse, decision support system, equipment management, multidimensional modeling.
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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 | 0.000 | 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 | 0.001 |
| 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".