Enhancing Project Evaluation and Review Technique Simulation through Artificial Neural Network-based Input Modeling
Bibliographic record
Abstract
Although a stochastic simulation study can eliminate the merge event bias in the project evaluation and review technique (PERT), the errors due to calculating the statistical descriptors of beta distributions with the three-point time estimates of PERT may still make the simulation results suspect. In order to enhance PERT simulation in terms of input modeling, this paper presents an artificial neural network (ANN)-based approach to estimate the true properties of the beta distributions from statistical sampling of actual data combined with subjective information. The minimum and maximum values along with the lower and upper quartiles are four time estimates used to uniquely define a beta distribution. The effects of shape parameters of beta distributions are closely examined, and the working range of shape parameters is defined. To construct an ANN model, data are prepared using random sampling techniques and Excel functions. Through exploring the training data provided, the ANN model has found the patterns between the inputs and the outputs, namely, the interactions and nonlinear relationships among the lower and upper quartiles and the shape parameters of the beta distributions. The ANN model was tested, validated, and compared with other packages for fitting beta distributions such as BetaFit, VIBES, and BestFit. The developed ANN-based input modeling method attempts to embed artificial intelligence into simulation and finds a new way to fit statistical distributions for activity duration in construction simulation, as demonstrated in a sample application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".