Assessment of Durability and Long Term Performance of High Density Polyethylene Drainboards in the Gotthard Railway Tunnels
Bibliographic record
Abstract
Drainage layers provide permanent relief of hydrostatic water pressure, while the waterproof liner prevents any ingress of water into the tunnel.The durability and aging resistance of drainage membranes are of primary concern.This paper describes advantages and concerns related to the usage of, and the design with, polymeric drainboards in tunnel construction.Common degradation mechanisms associated with HDPE (high density polyethylene) sheets are described.The stringent requirements for the Gotthard Alpine Railway Tunnel through the Swiss Alps, e.g., high ambient temperatures of up to 45 C and an expected service life of up to 100 years require outstanding aging resistance of polymeric drainage materials.The paper describes the methods deployed to investigate the long-term performance of HDPE drainboards, focusing on aging mechanisms.Details associated with the test procedures developed to reflect the specific properties of drainboards, as well as the results obtained, are presented.A summary table shows recommended product specifications needed to confine the aging properties of drainboards and to design a system performing adequately during the entire lifetime of the structure.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".