Application of the Fuzzy AHP in Selection of Most Appropriate Construction Method for Tuti - Bahri Bridge
Bibliographic record
Abstract
In presence of many kilometers of waterways and valleys in Khartoum State, bridges had become desperately needed for proper communication and development. There were many types of bridges in Sudan made of concrete and/or steel using various types of suspended, steel truss, arch and cable-stayed bridges which were constructed by different methods. The successes in bridge projects will be reached if the project achieved quality, cost saving and completed in optimum time schedule. Therefore, selecting appropriate bridge construction method is essential for the success of bridge construction projects. The Analytical Hierarchy Process (AHP) method has been widely used for solving multi-criteria decision-making problems. The Authors applied Fuzzy AHP, similar to the process suggested by Nang in 2008, to select the most appropriate construction method to be adopted for the Cable-stayed Tuti-Bahri Bridge Project which the Ministry of Infrastructures and Transportation of Khartoum State intends to construct at Khartoum city-center. The results were successfully applied for selection of the most appropriate bridge superstructure construction method among two methods. The hierarchy of the alternatives is suggested based on opinion of two Sudanese experts through questionnaires to obtain the basic criteria. Weights, from group of evaluations, are analyzed using excel spread sheets developed by the Authors. Applying Nang’s approach revealed logical procedure for selection of construction method that best suits Tuti-Bahri Bridge.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".