Integrating numerical tools in underground construction process
Bibliographic record
Abstract
Purpose The purpose of this paper is to show how the time frame for the execution of a construction project in Algeria is rarely respected because of organizational problems and uncertainties encountered while the execution is underway. Design/methodology/approach A case study on the construction of a metro station is used as a pilot project to show the effectiveness of replacing traditional construction processes by more innovative procedures. Concurrent engineering (CE) is applied to optimize the execution time of the underground structure. A numerical simulation is integrated into the construction process in order to update design parameters with real site conditions observed during the construction process. Findings The results show that the implementation of CE is efficient in reducing the completion time, with an 18 per cent reduction observed in this case study. A cost reduction of 20 per cent on the steel frame support and a total cost reduction of 3 per cent were obtained. Research limitations/implications The study demonstrates that the application of CE methods can be quite valuable in large, complex construction projects. Vulgarizing it as “the solution” to adjust time frame delay, control quality and cost, might be an issue for local construction enterprises in Algeria. Originality/value Using the concept of CE by overlapping different activities involved in a construction project and making use of simulation tools in the process at different stages of the execution have resulted in modifying the excavation method and hence reducing the completion times.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".