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Record W4413135615 · doi:10.1061/9780784486375.036

A Retrospective of 15 Years of Spray-Applied Geopolymer Mortar Linings

2025· article· en· W4413135615 on OpenAlexaboutno aff
Joseph R. Royer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsMortarGeopolymerMaterials scienceForensic engineeringComposite materialEngineeringFly ash

Abstract

fetched live from OpenAlex

For buried infrastructure, conventional dig and replace has become a very expensive option for rehabilitating aging pipes. Over the past 50 years, the use of trenchless technologies has provided an affordable means of extending the life of storm and sanitary pipes. Over the last 15 years, the use of spray-applied pipe lining (SAPL), utilizing cementitious mortars, specifically geopolymers, has become a more preferred method of choice for rehabilitating large-diameter pipes and other buried structures. This paper reviews the history of over 500,000 linear ft of large-diameter pipes (>36″) and structures that have been structurally lined using this method around the globe. An overview of the advances in materials, spray distance, pumping distance, and equipment will be reviewed in detail, showing the evolution of the technology. The environmental impact of geopolymers in reducing greenhouse gas emissions by more than 75% compared to traditional cementitious materials, as well as the additional environmental advantages of the installation process compared with traditional and other trenchless processes, will be examined. The advances in standards, including ASTM, NASSCO, WRc, and others will be reviewed. In addition, the movement towards accepted design methodologies backed by detailed structural testing will be reviewed. Finally, several of the most interesting projects are highlighted, including a test project for the USEPA and others in the City of Houston, two phases of restoration of a trunk sewer in Ontario totaling over 10,000 linear ft of lining, as well as several smaller culvert projects conducted in Florida.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.006

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.

Opus teacher head0.004
GPT teacher head0.205
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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