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Record W7117613634 · doi:10.1016/j.soildyn.2025.110047

Simplified assessment of corroded structures

2025· article· en· W7117613634 on OpenAlexaff
S.P. Tastani, F. Dameh, N. El-Joukhadar, S.J. Pantazopoulou

Bibliographic record

VenueSoil Dynamics and Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsYork University
Fundersnot available
KeywordsCorrosionStiffnessNonlinear systemReduction (mathematics)ReinforcementResidualDeformation (meteorology)HierarchyAttenuation

Abstract

fetched live from OpenAlex

Current seismic evaluation frameworks of reinforced concrete structures such as those of ASCE/SEI 41 (2023) and EN 1998-3 (2021), are calculation-intensive and require extensive information of reinforcement ratios and detailing. However, the guidelines do not consider the condition of reinforcement, which was proven to affect the member's residual mechanical properties, the hierarchy of failure modes and the consequences on seismic performance. Considering the condition of the reinforcement complicates further the problem of seismic assessment. This paper focuses on simple modifications of existing nonlinear assessment procedures to account for the effects of moderate corrosion on seismic performance. The methodology along with an application example of computational assessment procedures of a regular five-storey structure with a soft first storey are presented, considering corrosion damage in nonlinear time history and pushover analysis. It is shown that corrosion exacerbates the seismic demands by effectively reducing the structural stiffness, while at the same time deprecating the lateral load resistance and drift capacity of the affected components. A simple criterion to determine the increased drift demand resulting from the stiffness loss due to corrosion is also included for rapid assessment procedures.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.573

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 head0.004
GPT teacher head0.213
Teacher spread0.210 · 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
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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