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Record W4231666922 · doi:10.1017/9781316585146.003

Introduction

2017· book-chapter· en· W4231666922 on OpenAlexaboutno aff
Zdeněk P. Bažant, Jia‐Liang Le

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsnot available
Fundersnot available
KeywordsEvent (particle physics)Limit (mathematics)Structural failureEngineeringAeronauticsForensic engineeringMathematicsRisk analysis (engineering)BusinessStructural engineeringPhysics

Abstract

fetched live from OpenAlex

Without realistic failure mechanics, probabilistic analysis of structural safety is a fiction. The Problem of Tail of Probability Distribution Like most things in life, we must accept that the occurrence probability of any future event cannot be exactly zero. We must be contented with a structural failure probability that is negligible compared to other risks that people willingly take, such as car accidents. It is generally agreed that adequate safety of engineering structures is achieved by specifying a failure probability of 10 −6 per lifetime as the maximum admissible in design [Nordic Committee for Building Structures (NKB) 1978; Melchers 1987; Duckett 2005; Ellingwood 2006]. This probability limit is generally accepted for engineering structures, whether bridges or aircraft (Duckett 2005; Department of National Defense of Canada 2007), although for some nuclear plant structures an even smaller limit is required. The smallness of this probability limit is a source of great difficulty. To check the design merely by an experimental histogram, at least 10 8 tests of identical structures or specimens would be required. Even a direct computational verification would necessitate about 10 8 repetitions of Monte Carlo simulations with a fully realistic material model. Therefore, estimations of loads of such a small failure probability must rely on a model that is justified by a sound theory and is validated by experiments other than histogram testing. For many years, realistic theoretical models have been available for the probability of ductile and brittle failures. The Gaussian and Weibull distributions, respectively, fit this purpose. Failures of structures made of quasibrittle materials are more difficult to predict and have been researched only recently. The difficulty is that the quasibrittle failures are transitional in nature between ductile and plastic failures. Quasibrittle materials are heterogeneous materials with brittle constituents. At the scale of normal laboratory testing, they include concretes as the archetypical case, fiberpolymer composites, fiber-reinforced concretes, toughened ceramics, many rocks, coal, sea ice, wood, consolidated snow, particle board, rigid foams, particulate nanocomposites, biological shells, mortar, masonry, fiber-reinforced concrete, asphalt concrete (at low temperatures), stiff clays, silts, cemented sands, grouted soils, particle board, various refractories, bone, cartilage, dentine, dental ceramics, paper, carton, and cast iron.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.191
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1910.100

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 head0.073
GPT teacher head0.255
Teacher spread0.182 · 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
GenreOther

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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