Crane Mat Layout Optimization Based on Agent-Based Greedy and Reinforcement-Learning Approach
Bibliographic record
Abstract
The growing prevalence of modular construction, while it offers benefits in terms of productivity and sustainability, has led to increased use of heavy mobile cranes and related resources on construction sites. One significant resource associated with crane use is the crane mat, which offers practical mobile crane ground support against poor soil-bearing capacity. Due to increased use of crane mats, crane mat layout plans/drawings have become increasingly significant in today’s construction industry. The present work describes an automated crane mat optimization framework for preparing crane mat layout plans/drawings built on an agent-based greedy algorithm and reinforcement learning. The proposed framework employs these approaches to achieve the maximum area with the minimum number of crane mats. The proposed framework is found to decrease the time required for preparing a crane mat layout plan/drawing (approximately 97% time saving) with more uniform and efficient crane mat planning outcomes (approximately 63% crane mat material reduction).
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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 | 0.001 | 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".