MétaCan
Menu
Back to cohort
Record W4312047735 · doi:10.33430/v29n4thie-2022-0002

Design criteria for strut-and-tie modelling in Hong Kong practice

2022· article· en· W4312047735 on OpenAlexaboutno aff
T N Wong, R.K.L. Su

Bibliographic record

VenueHKIE Transactions · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersImpact Fund
KeywordsEurocodeEngineeringStructural engineeringBridge (graph theory)Flexural strengthSet (abstract data type)Design methodsSoftwareCode (set theory)Construction engineeringComputer scienceCivil engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Strut-and-tie modelling has proved a very useful method in the analysis and design of non-flexural components of reinforced concrete members. Examples of design codes encompassing the strut-and-tie model (STM) provisions include Eurocode 2 (EC2), the fib Model Code 2010 (MC2010), the Canadian Standard (CSA A23.3-04), the American bridge design specifications (AASHTO LRFD 2020), the American Standard (ACI 318-14 and -19) and the Australian Standard (AS 3600:2018). Nevertheless, the application of different assumptions, model types and methodologies means that the strength acceptance criteria for struts, nodes and bearings vary within the literature and design codes. Unifying the STM design criteria is thus encouraged, to facilitate worldwide use of the STM in the design of non-flexural components, particularly for places such as Hong Kong that have not yet developed the localised design criteria for the STM. In this paper, the proposals set out in the literature and the provisions in the above-stated design codes are reviewed and compared with each other. Calibrating with the existing literature and design provisions, unified STM design criteria for struts, nodes and bearings with localisation are proposed for application in Hong Kong. The proposed design criteria are then applied to the STM design of a cantilever deep beam.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.908
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.274
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueHKIE TransactionsSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207