Machine learning predictions for bending capacity of ECC-concrete composite beams - hybrid reinforced with steel and FRP bars

Aug 5, 2026

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This paper develops and compares machine learning models to predict the bending capacity of hybrid reinforced ECC-concrete composite beams using steel and FRP bars. Five algorithms—SVR, XGBoost, MLP, RF, and ERT—are trained on 150 experimental data points from prior literature, then evaluated for predictive accuracy. SHAP analysis quantifies feature contributions, showing the equivalent reinforcement ratio, FRP bar design strength, and beam cross-section height as key parameters while concrete compressive strength has limited impact. Based on the best model, a GUI supports practical engineering application.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Abstract

Keywords

Introduction

Methodology and Models

Data and Model Training

Feature Importance via SHAP

Results and Discussion

Prediction Performance

Key Influential Parameters

Graphical User Interface for Practical Use

Case Studies in Construction Materials 21 (2024) e03670 Contents lists available at ScienceDirect Case Studies in Construction Materials journal [homepage:](homepage: www.elsevier.com/locate/cscm)[ www.elsevier.com/locate/cscm](homepage: www.elsevier.com/locate/cscm) | Case study
Machine learning predictions for bending capacity of
ECC-concrete composite beams hybrid reinforced with steel and FRP bars
Wenjie Gea , Feng Zhang a , Yi Wang a , Ashraf Ashour b , Laiyong Luoc , Linfeng Qiud , Shihu Fue , Dafu Caoa, *
a College of Civil Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China b Faculty of Engineering and Digital Technologies, University of Bradford, Bradford BD71DP, UK c Jiangsu Yangjian Group Co., Ltd, Yangzhou, Jiangsu 225002, China
d Nantong Construction Engineering Quality Supervision Station, Nantong, Jiangsu 226000, China e Yangzhou Jianwei Construction Engineering Testing Center Co., Ltd., Yangzhou, Jiangsu 225002, China | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords: Machine learning Bending capacity
ECC-concrete composite beams Hybrid reinforcement | | This paper explores the development of the most suitable machine learning models for predicting the bending capacity of steel and FRP (Fiber Reinforced Ploymer) bars hybrid reinforced ECC (Engineered Cementitious Composites)-concrete composite beams. Five different machine learning models, namely Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Random Forest (RF), and Extremely Randomized Trees (ERT), were employed. To train and evaluate these predictive models, the study utilized a database comprising 150 experimental data points from the literature on steel and FRP bars hybrid reinforced ECCconcrete composite beams. Additionally, Shapley Additive Explanations (SHAP) analysis was employed to assess the impact of input features on the prediction outcomes. Furthermore, based on the optimal model identified in the research, a graphical user interface (GUI) was designed to facilitate the analysis of the bending capacity of hybrid reinforced ECC-concrete composite beamsin practical applications. The results indicate that the XGBoost algorithm exhibits high accuracy in predicting bending capacity, demonstrating the lowest root mean square error, mean absolute error, and mean absolute percentage error, as well as the highest coefficient of determination on the testing dataset among all models. SHAP analysis indicates that the equivalent reinforcement ratio, design strength of FRP bars, and height of beam cross-section are significant feature parameters, while the influence of the compressive strength of concrete is minimal. The predictive models and graphical user interface (GUI) developed can offer engineers and researchers with a reliable predictive method for the bending capacity of steel and FRP bars hybrid reinforced ECCconcrete composite beams. | 1. Introduction With the continuous advancement of construction engineering, there has been an increasing demand for the structural * Corresponding author. E-mail address: [dfcao@yzu.edu.cn](dfcao@yzu.edu.cn) (D. Cao). [https://doi.org/10.1016/j.cscm.2024.e03670](https://doi.org/10.1016/j.cscm.2024.e03670) Received 27 April 2024; Received in revised form 20 August 2024; Accepted 21 August 2024 Available online 22 August 2024 2214-5095/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license ([http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/)). performance, challenging the traditional reinforced concrete (RC) structures to meet the requirements of bearing capacity, durability, and ductility [1]. Consequently, innovative materials and structural systems are being explored to enhance the performance and longevity of these structures. Concrete, as the most widely used building

FAQs

Which machine learning algorithms were used to predict bending capacity?

The study employed Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Random Forest (RF), and Extremely Randomized Trees (ERT).

How was the training and evaluation database constructed?

Model training and evaluation used 150 experimental data points collected from literature covering steel and FRP bars hybrid reinforced ECC-concrete composite beams.

What did SHAP analysis identify as the most important input features?

SHAP indicates that the equivalent reinforcement ratio, FRP bar design strength, and the height of the beam cross-section significantly affect predictions, while concrete compressive strength has minimal influence.

Aug 5, 2026

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