Intelligent Data Driven Ensemble Approaches for Bending Strength Prediction of Ultra-High Performance Concrete Beams

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21610%2F25%3A00384999" target="_blank" >RIV/68407700:21610/25:00384999 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.53941/bci.2025.100003" target="_blank" >https://doi.org/10.53941/bci.2025.100003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.53941/bci.2025.100003" target="_blank" >10.53941/bci.2025.100003</a>

Result language

angličtina

Original language name

Intelligent Data Driven Ensemble Approaches for Bending Strength Prediction of Ultra-High Performance Concrete Beams

Original language description

Existing code equations for predicting the bending capacity of ultra high performance fiber reinforced concrete (UHPFRC) beams often show large scatter, with coefficients of variation (CoV) exceeding 30 to 60 %, leading to both overly conservative and unconservative estimates. This study develops a novel and explainable machine learning (ML) framework for accurate bending capacity prediction, representing the first systematic benchmarking of advanced ensemble ML methods against international and national design codes. An updated database of 264 experimental UHPFRC beam tests was compiled from the literature and partitioned into training (70%), validation (15%), and testing (15%) subsets. Six ensemble algorithms were optimized using Bayesian hyperparameter tuning with 10 fold cross validation, namely Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Extreme Gradient Boosting (XGBoost). The best performing models, CatBoost and XGBoost, achieved excellent predictive accuracy on unseen test data (R2 ~ 0.96 to 0.97, RMSE ~ 15 to 18 kN.m, CoV ~ 10%) with essentially no systematic bias. These results clearly outperform code equations (R2 ~ 0.60–0.70, CoV ~ 28–64%, and biases up to 40 %). Interpretability analysis using Shapley Additive Explanations (SHAP) confirmed that effective depth and reinforcement ratio are the dominant predictors of bending strength, followed by steel yield strength and section properties, while UHPC compressive strength and fiber parameters had relatively minor influence within the dataset range. The novelty of this study lies in presenting an integrated and interpretable ML framework that not only achieves superior predictive performance but also provides mechanistic insight into UHPFRC beam behavior. The proposed approach offers a reliable data driven complement to current design codes and has potential for practical adoption in structural engineering design and code development.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

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

2025

Confidentiality

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

Name of the periodical

Bulletin of Computational Intelligence

ISSN

3083-4643

e-ISSN

3083-4643

Volume of the periodical

1

Issue of the periodical within the volume

1

Country of publishing house

AU - AUSTRALIA

Number of pages

22

Pages from-to

31-52

UT code for WoS article

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EID of the result in the Scopus database

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