Investigating the Optimal Design Variables of Concrete Slabs with Impact Resistance by Development a Multi-Objective Model to Save Cost-Time of Construction
Bibliographic record
Abstract
Globally, construction industry plays a key role in development of an economy of any country. On the other side, construction projects suffer extensive delays and cost overruns exceeding by that the budgeted money and the estimated time. Therefore, adopting techniques like optimization technique in construction media became a necessity for preventing the actual cost and execution time of projects from exceeding the planned cost and estimated time, respectively. Since, concrete is the most essential construction and the most used material in construction projects construction where it influences extremely gross domestic products of numerous nations in the world, a multi-objective mathematical model with mixed linear and binary constraints is developed to specify the optimal solution of differently designed concrete slabs to impact loads. Eight concrete slabs had various design parameters, such as concrete thickness, steel fiber ratio, reinforcement ratio, and steel stiffener thickness are used in this study. The mathematical model comprised two objective functions: minimization cost and time, and had a number of constraints like concrete ingredients, load, deflection, and weight. The outputs of the developed model revealed a variety of slab design combinations with different cost, time, and deadloads depending on the limitations of serviceability loads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".