Proceedings of the 33rd International Symposium on Automation and Robotics in Construction (ISARC)
Bibliographic record
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
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.033 |
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".