Long-Term Monitoring and Analysis of Full Scale Concrete Pipe Test Beds
Bibliographic record
Abstract
Current design standards for concrete pipe bedding in Canada utilize the Indirect Design Method for the design of backfill and bedding. This approach does not fully account for the strength of the pipe and does not take into account the nature of the native soil, as every installation is considered to be the same. Consequently, the Indirect Design Method is overly conservative in many cases where the pipe is installed in competent ground and the anticipated level of live loads is minimal. The Standard Installation Direct Design (SIDD) system for design and installation of concrete pipes was developed through the American Concrete Pipe Association's (ACPA) long-term research program. The method takes into account the inherent strength of the concrete pipe as well as insitu soil conditions. The SIDD design minimizes the use of imported material while making maximum use of native soils. This paper describes the design, installation, long-term monitoring and Finite Element Modeling of full-scale SIDD Type 4 installations. Type 4 installations do not use any imported material and require minimal backfill compaction. Pipe diameter is selected to be 600mm with a burial depth of 1.8m. The test sections are located in heavy traffic areas and simulate the worse case scenario for pipe installation. The results from a 16-month monitoring period are reported and compared with predictions from the ASCE 15–93 specification, Marston-Spangler theory and two finite element analyses. It was concluded based on nearly 40,000 data points that the SIDD method illustrates well the stress envelope around a buried rigid pipe installed using the cut-and-cover construction 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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".