Applying Regression Analysis to Predict and Classify Construction Cycle Time
Bibliographic record
Abstract
Regression techniques are commonly used for addressing complicated prediction and classification problems in civil engineering thanks to its simplicity. For a given dataset, the linear regression from the input space to the output variables can be achieved by using the "least square error" approach, which minimizes the difference between the predicted and actual outputs. The "least mean square" rule can also be used as a generic approach to deriving solutions onlinear or non-linear regressions. The paper addresses the fundamental algorithms of "least square error" and "least mean square" in order to facilitate the prediction and classification of cycle times of construction operations. The classic XOR problem is selected to verify and validate their performances. A viaduct bridge was installed by launching precast girders with a mobile gantry sitting on two piers. The effectiveness of regression techniques in classifying and forecasting the cycle time of installing one span of viaduct considering the most relevant input factors in connection with operations, logistics and resources are demonstrated.
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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 | 0.001 |
| 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".