Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel

Aug 4, 2026

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Chloride-induced deterioration of reinforcing steel bars has become a heavily studied problem due to its strong impact on the structural reliability of aging infrastructure. The paper presents a tensile-testing program for 284 artificially corroded 25 mm deformed Grade500E bars and develops regression-based machine learning models to predict corroded-bar mechanical characteristics. Models are trained and evaluated using 1387 tensile tests compiled from 25 literature programs, using 19 input parameters to predict nine key properties. Across models, ANFIS shows the best overall predictive ability, while ensemble tree-based methods yield consistently high performance across the response variables.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Introduction

Corrosion effects on mechanical properties

Evidence from experimental testing

Methodology and experimental program

Tensile testing dataset and input parameters

Predictive modeling with regression algorithms

ANFIS and ensemble tree-based learning

Results and model performance

Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel Citation for published version (APA): Matthews, B. , Palermo, A. , Logan, T. , & Scott, A. (2024) . Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel. Construction and Building Materials, 438, Article 137023. Advance online publication. [https://doi.org/10.1016/j.conbuildmat.2024.137023](https://doi.org/10.1016/j.conbuildmat.2024.137023) Document license: CC BY DOI: 10.1016/j.conbuildmat.2024.137023 Document status and date: E-pub ahead of print: 09/08/2024 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.tue.nl/taverne](www.tue.nl/taverne) Take down policy If you believe that this document breaches copyright please contact us at: [openaccess@tue.nl](openaccess@tue.nl) providing details and we will investigate your claim. Download date: 07. Jul. 2024 Construction and Building Materials 438 (2024) 137023 Contents lists available at ScienceDirect Construction and Building Materials journal [homepage: www.elsevier.com/locate/conbuildmat](homepage: www.elsevier.com/locate/conbuildmat) | Experimental testing and predictive machine learning to determine the mechanical characteristics of corroded reinforcing steel
Benjamin Matthewsa, * , Alessandro Palermo b , Tom Logan c , Allan Scott c
a Department of the Built Environment, Eindhoven University of Technology, Eindhoven, Netherlands b Department of Structural Engineering, University of California San Diego, San Diego, USA
c Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Corrosion Reinforced concrete Mechanical properties Machine learning Tensile testing | | Chloride-induced deterioration of reinforcing steel bars has become a densely researched topic over the past several decades because of the severe ramifications to the structural reliability of aging infrastructure. The evergrowing volume of experimental and field data continually enables advances in the field through deeper micromacro analyses and various modeling applications. The purpose of this paper is twofold. First, an experimental program is introduced, describing the tensile testing of 284 artificially corro

FAQs

Why is chloride-induced corrosion of reinforcing steel important for structural reliability?

Chloride-induced deterioration is tightly linked to severe consequences for the structural reliability of aging infrastructure, motivating extensive experimental and modeling research.

What experimental program and dataset are used to study corroded reinforcing steel?

The study includes tensile testing of 284 artificially corroded 25 mm diameter deformed Grade500E reinforcing bars, and it trains models using 1387 tensile tests compiled from 25 other experimental programs.

Which machine learning approach performs best for predicting the mechanical properties of corroded bars?

The adaptive-neuro fuzzy inference system (ANFIS) shows the strongest individual predictive ability across all compared models, while ensemble tree-based learning provides consistently high performance across multiple response variables.

Aug 4, 2026

Upload by Eliana