Probabilistic prediction of Load–Displacement curves of corroded strands

This study proposes a probabilistic method for predicting the non-linear mechanical behavior of corroded steel strands. The proposed method follows a four-step process: (1) Developing sophisticated finite element models that accurately represent various types of corrosion; (2) Constructing a multi-s...

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Main Authors: Seungjun Lee, Jaebeom Lee, Chi-Ho Jeon, Young-Joo Lee
Format: Article
Language:English
Published: Elsevier 2025-04-01
Series:Developments in the Built Environment
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Online Access:http://www.sciencedirect.com/science/article/pii/S2666165925000444
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author Seungjun Lee
Jaebeom Lee
Chi-Ho Jeon
Young-Joo Lee
author_facet Seungjun Lee
Jaebeom Lee
Chi-Ho Jeon
Young-Joo Lee
author_sort Seungjun Lee
collection DOAJ
description This study proposes a probabilistic method for predicting the non-linear mechanical behavior of corroded steel strands. The proposed method follows a four-step process: (1) Developing sophisticated finite element models that accurately represent various types of corrosion; (2) Constructing a multi-surrogate model using Gaussian process regression; (3) Predicting load–displacement curves based on a theoretical model; and (4) Implementing a probabilistic analysis using Monte Carlo simulation and kernel density estimation. Validation was performed through two approaches: (i) scenario-based synthetic simulations of 1000 corrosion cases, and (ii) experimental tensile tests on 39 real-world corroded seven-wire strand specimens. Predictions closely matched experimental results, capturing tensile strength and yield displacement within 99 % prediction bounds for 94.87 % and 89.74 % of specimens, respectively. This framework provides an effective tool for assessing corroded strands, enabling the probabilistic evaluation of prestressed concrete girders and supporting maintenance strategies for corrosion-affected infrastructure.
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publishDate 2025-04-01
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series Developments in the Built Environment
spelling doaj-art-d01c1adf5f0540719e09e5c55f6080c52025-08-20T03:18:19ZengElsevierDevelopments in the Built Environment2666-16592025-04-012210064410.1016/j.dibe.2025.100644Probabilistic prediction of Load–Displacement curves of corroded strandsSeungjun Lee0Jaebeom Lee1Chi-Ho Jeon2Young-Joo Lee3Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of KoreaDivision of Physical Metrology, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34113, Republic of Korea; Precision Measurement Major, University of Science and Technology (UST), Daejeon, 34113, Republic of Korea; Corresponding author. Division of Physical Metrology, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34113, Republic of Korea.Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang, 10223, Republic of KoreaDepartment of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea; Corresponding author. Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.This study proposes a probabilistic method for predicting the non-linear mechanical behavior of corroded steel strands. The proposed method follows a four-step process: (1) Developing sophisticated finite element models that accurately represent various types of corrosion; (2) Constructing a multi-surrogate model using Gaussian process regression; (3) Predicting load–displacement curves based on a theoretical model; and (4) Implementing a probabilistic analysis using Monte Carlo simulation and kernel density estimation. Validation was performed through two approaches: (i) scenario-based synthetic simulations of 1000 corrosion cases, and (ii) experimental tensile tests on 39 real-world corroded seven-wire strand specimens. Predictions closely matched experimental results, capturing tensile strength and yield displacement within 99 % prediction bounds for 94.87 % and 89.74 % of specimens, respectively. This framework provides an effective tool for assessing corroded strands, enabling the probabilistic evaluation of prestressed concrete girders and supporting maintenance strategies for corrosion-affected infrastructure.http://www.sciencedirect.com/science/article/pii/S2666165925000444Pitting corrosionCorroded strandsMechanical behaviorProbabilistic predictionSurrogate model
spellingShingle Seungjun Lee
Jaebeom Lee
Chi-Ho Jeon
Young-Joo Lee
Probabilistic prediction of Load–Displacement curves of corroded strands
Developments in the Built Environment
Pitting corrosion
Corroded strands
Mechanical behavior
Probabilistic prediction
Surrogate model
title Probabilistic prediction of Load–Displacement curves of corroded strands
title_full Probabilistic prediction of Load–Displacement curves of corroded strands
title_fullStr Probabilistic prediction of Load–Displacement curves of corroded strands
title_full_unstemmed Probabilistic prediction of Load–Displacement curves of corroded strands
title_short Probabilistic prediction of Load–Displacement curves of corroded strands
title_sort probabilistic prediction of load displacement curves of corroded strands
topic Pitting corrosion
Corroded strands
Mechanical behavior
Probabilistic prediction
Surrogate model
url http://www.sciencedirect.com/science/article/pii/S2666165925000444
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AT jaebeomlee probabilisticpredictionofloaddisplacementcurvesofcorrodedstrands
AT chihojeon probabilisticpredictionofloaddisplacementcurvesofcorrodedstrands
AT youngjoolee probabilisticpredictionofloaddisplacementcurvesofcorrodedstrands