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Record W2728401299 · doi:10.1061/9780784480793.019

Unified Design Approach for Rock Fissure Grouting—Best Design Practice for Pre-Grouting of Rock Tunnels in Sweden

2017· article· en· W2728401299 on OpenAlexaff
Mikael Creütz, Magnus Zetterlund, Magnus Eriksson, Thomas Janson, Thomas Dalmalm

Bibliographic record

VenueGrouting 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsProcurementProcess (computing)Engineering design processCivil engineeringEngineeringComputer scienceConstruction engineeringMechanical engineeringBusiness

Abstract

fetched live from OpenAlex

In general, all tunneling contracts in Sweden are based on performance contracts. There has been a large increase in the knowledge and understanding during the last decades in Sweden regarding sealing of rock tunnels using mostly cementitious rock fissure grouting. This increased know-how derives from extensive research and execution of continuous pre-grouting throughout different rock tunneling works in Sweden. However, there is still a large variety in grouting designs as well as in performance between, and even, within projects. In order to make sealing of tunnels more efficient the Swedish Transport Administration has initiated a project to establish a unified design approach for rock fissure grouting in hard crystalline rock. The aim of the design approach is to have a coherent and structured process and methodology for grouting design, resulting in an appropriate, similar and transparent design approach independent of designer. The purpose is thus to introduce explicit guidelines for grouting design and its presentation as grouting classes that will be used in technical specifications, bill of quantities and prognosis for further procurement of grouting works in performance contracts. The improved tendering document should result in reduced costs due to efficient design works followed by an easier procurement process for performance contracts along with improved grouting performance and hopefully also less claims/disputes. The design approach is not yet implemented. However, the design approach is based on known theories and experience that are now compiled into one comprehensive methodology.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.320
Teacher spread0.222 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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