Prediction of Moment Capacity of Ultra-High-Performance Concrete Beams Using Explainable Extreme Gradient Boosting Machine Learning Model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F24%3A00377294" target="_blank" >RIV/68407700:21610/24:00377294 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Prediction of Moment Capacity of Ultra-High-Performance Concrete Beams Using Explainable Extreme Gradient Boosting Machine Learning Model

Original language description

The utilisation of Ultra-High-Performance Concrete (UHPC) in structural engineering presents significant design challenges, often necessitating the use of costly and complex finite element analyses due to strongly non-linear material behaviour. To address these issues, this study introduces an explainable machine learning (ML) model employing the eXtreme Gradient Boosting (XGBoost) algorithm, aimed at accurately predicting the bending capacity of UHPC beams. Demonstrating robust performance, the XGBoost model achieved a high coefficient of determination (R2) of 99.5% (training) and 97.6% (testing), outperforming conventional analytical models (FHWA, SIA 2052,and JGJ/T 465) cited in the literature. Further analysis employing the SHapley Additive Explanation (SHAP) framework provided insights into the model’s decision-making process, revealing that the effective depth, steel reinforcement ratio, and steel yield strength significantly influence the predicted bending capacity, while the aspect ratio of fibres has a minimal impact. This research highlights the potential of the XGBoost model to revolutionise prediction, analysis, and design processes within performance-based design frameworks, offering a promising alternative to traditional non-linear analysis methods for UHPC beams.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

<a href="/en/project/GA24-10892S" target="_blank" >GA24-10892S: Machine Learning for Multiscale Modelling of Spatial Variability and Fracture for Sustainable Concrete Structures</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

1st International Conference on Synergy between Multi-physics/Multi-scale Modeling and Machine Learning

ISBN

978-9926-8888-1-7

ISSN

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e-ISSN

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Number of pages

4

Pages from-to

84-87

Publisher name

Association of Computational Mechanics in Bosnia and Herzegovina

Place of publication

Sarajevo

Event location

Prague

Event date

Jun 19, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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