COMPARATIVE STUDY OF GREENHOUSE GAS EMISSIONS FROM HAND TUNNELING AND PILOT TUBE METHOD UNDERGROUND CONSTRUCTION METHODS
Bibliographic record
Abstract
ABSTRACT The negative effects of greenhouse gas (GHG) emissions, such as climate change and global warming, have become major environmental concerns, especially for the construction industry, which is the third-highest source of GHG emissions among industrialized countries. Presently, underground utility projects are considered one of the most common types of construction, primarily due to aging infrastructure across North America and the subsequent rehabilitation of old pipelines and installation of new pipelines and facilities. Given the increasing demand being placed on the industry, the need to study airborne emissions associated with different underground construction technologies has risen, which will be helpful in selecting the most sustainable underground construction methods. This study investigates pollutant emission from two common trenchless methods used in underground construction, hand tunneling and pilot-tube method (PTM), through their varying GHG footprint sources and emissions measured by the United States Environmental Protection Agency (EPA). This paper analyzes a case from Edmonton, Canada, in which both PTM and hand tunneling were used by comparing the suggested indexes, including HC, CO, NOx, PM, CO2, and SO2. In this case study, both methods were used in the installation of a new 68-cm diameter (27 in.) clay sewer line with an overburden depth of 12.9 m (42 ft) and length of 60 m (197 ft). Results indicated that the amount of airborne emissions was reduced between 17% and 36% through the use of PTM compared to the traditional hand tunnelling method.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".