Analytical Hierarchy Process Decision Support System (AHP-DSS) for Trenchless Technology
Bibliographic record
Abstract
Analytical Hierarchy Process Decision Support System (AHP-DSS) for Trenchless Technology Mohamed Salah, Soliman Abu Samra, Ossama Hosny Pages 1-9 (2016 Proceedings of the 33rd ISARC, Auburn, USA, ISBN 978-1-5108-2992-3, ISSN 2413-5844) Abstract: Among the increasing complexities and surface development, underground utilities installation, renewal and repair remain one of the most challenging projects worldwide. In addition, the crucial need for a minimal surface disruption is what even makes it more thought provoking for contractors/specialists to maintain. That is why, trenchless technology has been an economical choice for many contractors/specialists, especially in urban areas, to guarantee less restoration costs, social, and environmental impact and higher accuracy with less time compared to the open cut and cover method. This paper aims to introduce a framework, utilizing a fully automated Analytical Hierarchy Process engine, which supports the contractors in their selection for the most appropriate trenchless method, taking the project characteristics and site conditions into consideration. The framework features through four different modules as follows: (1) Input Module where the user enters the project attributes through the AHP-DSS user interface. (2) Central Database Module that contains the considered trenchless methods, project attributes limits & their weights and trenchless methods & their scores. (3) Analytical Hierarchical-based Engine that runs simultaneously with the central database module to provide the user with the most suitable construction method. (4) Trenchless Technology Method Module that shows the most suitable method that suits the pre-defined user inputs. Spreadsheet modelling has been used for developing the Analytical Hierarchal Process Decision-Support System (AHP-DSS). A case study composed of 20 projects with various characteristics and conditions has been used for validating and verifying the model. The results showed a percentage of error less than 10% compared to the actual executed results Keywords: AHP, Decision support system, Modeling, Trenchless technology DOI: https://doi.org/10.22260/ISARC2016/0001 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".