Machine Learning Framework to Predict Last Planner System Performance Metrics
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
Despite numerous attempts toward enhancing performance, the construction industry is still behind in this term. Various technological advancements are at the disposal of construction researchers and practitioners to address this issue. Machine learning techniques are one example of technologies that have become readily accessible to general users thanks to the efforts of researchers in construction planning and control and other fields. Accordingly, the performance in the construction industry may be improved by employing machine learning techniques for developing performance indicators to forecast possible issues or take corrective measures proactively. While some studies have applied machine learning in various aspects of lean construction, no research has yet employed machine learning to predict specific performance metrics. This study, therefore, aims at developing a framework to predict the Last Planner System (LPS) metrics. This will allow optimizing performance on a near real-time basis. A framework for implementing such an approach toward project control is presented, and several predictive models are created, compared, and refined. This approach opens an entirely new avenue for applications of machine learning and data mining techniques in lean construction project planning and control.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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