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Record W4297828059 · doi:10.1061/9780784484395.034

New ASCE/COPRI Design Standards for Piers and Wharves

2022· article· en· W4297828059 on OpenAlexaff
Omar Jaradat, Julian Cajiao, Daryl English, Anthony L. Farmer, Julie Galbraith, Rune Iversen, Bill Paparis, David B. Pryor, Raj S. Varatharaj

Bibliographic record

VenuePorts 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsComputer scienceCivil engineeringGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

To analyze and design piers and wharves, engineers must reference and integrate more than 40 national and international existing standards and guidelines. In 2019, the American Society of Civil Engineers’ (ASCE) Coasts, Oceans, Ports and Rivers Institute (COPRI) voted to create a standards committee to develop a new Design Standards for Piers and Wharves to address the lack of a comprehensive standard or guidance in the United States. The purpose of this committee is to create a consensus-driven, single resource document that provides consistent analysis and design guidance for determining loads, load combinations, and load factors to design for mooring, berthing, and metocean conditions. This new ASCE/COPRI Design Standards for Piers and Wharves will apply to the design, construction, alteration, expansion, upgrade, repair, rehabilitation, and replacement of piers and wharves or near-shore marine structures. The purpose of this standard is to provide minimum requirements, loads, load combinations, hazard levels, associated criteria, and performance goals based on current scientific and engineering knowledge, experience, and industry practice. This paper provides a summary of the new ASCE/COPRI Design Standards for Piers and Wharves by topic area and includes technical challenges and identifies potential research needs.

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)0.017
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0050.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0120.007

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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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 designNot applicable
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

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