Comparison of Trenchless Technologies and Open Cut Methods in New Residential Land Development
Bibliographic record
Abstract
Nowadays, with the expansion of population in urban areas, development of new residential and commercial areas is essential to meet the needs of this ever-expanding population. Urban water and wastewater systems are fundamental infrastructure in this development, and are very important for high quality of life and strong urban economy. Also, there is growing attention to consider different factors, such as environmental, cost, social impacts, safety and seasonality in the development of infrastructures. It is considered indispensable to improve construction practices and develop infrastructures in ways that considers all above mentioned concerns in urban construction. As the world moves towards providing a better and cleaner environment for future generations, there is an urgent need to quantify and reduce the emission footprints of industries. The construction industry, which consumes a large quantity of fossil fuels, is one of the targeted industries for which researchers aim to evaluate proper alternatives to traditional construction methods in order to reduce these emissions. Underground utility installations, especially in the development of residential communities in urban areas, are one of the largest construction projects across North America and, consequently, one major source of emissions. Moreover, studying cost as essential element is important in the development of underground infrastructure. Project owners and decision makers look for economical methods for installing underground infrastructure and renewing underground utility pipes.This dissertation demonstrates a comparison between the traditional open cut option in underground utility projects and trenchless methods (auger boring and HDD) through two case studies in new residential development area in Edmonton, Alberta, which consists of three main lines: water, sanitary, and storm. The results show that GHG emissions generated from open cut were significantly higher compared to the estimated trenchless alternatives. Also compared to open trench, trenchless techniques are more expensive in Edmonton, Alberta. However, productivity, and constructability of trenchless methods in in cold areas such as Alberta, these technologies would be considerable alternatives to open cut.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".