Using Computer Simulations to Plan Construction Projects Accurately
Bibliographic record
Abstract
The three main objectives in construction projects are completing the project on time, within budget, and with good quality. Each construction project is unique and unpredictable making it beneficial to model the project before executing it. There are many ways to model a construction project; however, computer models are ideal. It is very costly and time consuming to experiment with the actual system. Therefore, by using a computer simulation, accurate data can be collected from the project without the time and cost drawbacks. The specific construction project researched is based on a real project from Fort Mcmurray Alberta, Canada. The construction project involved the delivery and erection of three different types of steel in a construction site. Once the steel has been delivered, it needs to be stored and then carried by forklift to one of two cranes to be erected. A schedule was provided for which days each type of material was expected to be delivered and erected, however this schedule did not account for the 20% chance that any delivery could be delayed by one day or the 10% chance that deliveries could be delayed by two days. A model project was created on Simphony.NET with the assumptions that work could commence the entire day (24 hours), the site has unlimited storage, and a delay in one delivery does not delay all the deliveries after it. The schedule for the project was then modified to reflect the results of the simulation. The modified schedule showed that several deliveries of materials were delayed. However, due to the model’s assumptions and the time for erection being relatively short, the planned schedule for the erection of the materials was not delayed. By using the data collected from the computer simulation it was possible to more accurately plan the schedule for this construction project.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".