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Record W4411685933 · doi:10.1016/j.softx.2025.102238

Rational-RC: A Python package for probabilistic life-cycle deterioration modelling of reinforced concrete structures

2025· article· en· W4411685933 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoftwareX · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
FundersMitacsUniversity of Saskatchewan
KeywordsPython (programming language)Reinforced concreteComputer scienceProbabilistic logicProgramming languageStructural engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Maintaining the durability of reinforced concrete (RC) structures is crucial for sustainable infrastructure management. Rational-RC is a Python package that provides a probabilistic framework for modelling the life-cycle deterioration of RC structures. Designed with a modular, object-oriented architecture, it enables flexible integration of key deterioration processes, including membrane degradation, chloride ingress, carbonation, corrosion, and cracking within a unified limit-state reliability framework. Using site-specific field data, users can calibrate deterioration models to generate staged probabilities of failure, informing condition-based maintenance strategies that optimize costs and extend service life. The framework can be extended to interact with structural models, and scaled for simulation at both the element and network levels. Its visualization tools and extensible design make it a powerful tool for researchers and practitioners aiming to tackle the challenges of aging infrastructure with advanced computational approaches.

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.

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)0.001
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: none
Teacher disagreement score0.625
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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