Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete - ML Algorithms for Strength Estimation

Aug 3, 2026

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Machine learning is applied to predict the mechanical properties of steel fibre-reinforced self-compacting concrete (SFRSCC), a composite that merges the high fluidity of self-compacting concrete with the toughness of steel fibre-reinforced concrete. Model performance is evaluated using datasets compiled from 15 literature sources (161 data groups) with inputs describing mixture design and steel fibre characteristics. Support vector regression (SVR) and artificial neural networks (ANN) estimate flexural strength and compressive strength, with comparisons against reported experimental results to identify the most accurate approach.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Background and Motivation

Machine Learning Approaches

Data Processing

Dataset Description

Input and Output Variables

Model Development and Evaluation

SVR and ANN Setup

Cross-Validation and Comparison

Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms Tianyi Cui(B) , Sivakumar Kulasegaram , and Haijiang Li Cardiff University, Cardiff CF24 3AA, UK [cuit2@cardiff.ac.uk](cuit2@cardiff.ac.uk) Abstract. With the development of big data processing technology and the continuous improvement ofcomputer operation ability, machine learning has achieved remarkable results in recent years. Applying machine learning to solve engineering problems is gaining more attention from researchers. Steel ﬁbre-reinforced self-compacting concrete (SFRSCC) is a new type of composite material prepared by combining the advantages of the high ﬂuidity of self-compacting concrete (SCC) and the high toughness of steel ﬁbre-reinforced concrete. However, the performance of SFRSCC is inﬂuenced by many factors such as water-binder ratio, mineral powder content and steel ﬁbre content. This study aims to predict the mechanical properties of SFRSCC mixes based on datasets collected from the literature. In the presented work, the machine learning algorithms are employed to investigate the effect of SCC compositions and steel ﬁbre on the performance of SFRSCC. The models used for the prediction are support vector regression (SVR) and artiﬁcial neural network (ANN) . In both models, input variables are set to be water to binder ratio, sand to aggregate ratio, maximum size of coarse aggregate, amount of other mix components (e.g., superplasticizers, limestone powder, ﬂyash), volume fraction and aspect ratio of steel ﬁbre, and curing age. The output variables are ﬂexural strength and compressive strength of SFRSCC specimens. The performances of machine learning models are evaluated by comparing the predicted results with experimental results obtained from the literature. Furthermore, a comparative study is performed to select the best-proposed model with better accuracy. Keywords: Machine Learning · Steel Fibre Reinforced Concrete · Self-compacting Concrete · Flexural Strength · Compressive Strength 1 Introduction Concrete is the most widely used material in the civil engineering design ofinfrastructure and building construction industries. With the development of science and technology, numerous new buildings and structures have emerged, and the demand for better concrete performance has continued to increase. Steel ﬁbre-reinforced self-compacting concrete (SFRSCC) combines the advantages of self-compacting concrete (SCC) and steel ﬁbrereinforced concrete (SFRC) . On the one hand, as an improvement of SCC, it retains the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Jdrzejewska et al. (Eds.): SynerCrete 2023, RILEM Bookseries 44, pp. 703–711, 2023 . [https://doi.org/10.1007/978-3-031-33187-9](https://doi.org/10.1007/978-3-031-33187-9_65)[_](https://doi.org/10.1007/978-3-031-33187-9_65)[65](https://doi.org/10.1007/978-3-031-33187-9_65) 704 T. Cui et al. advantages of high ﬂuidity of SCC without the need for manual vibration; on the other hand, it has higher tensile strength, impact resistance and anti-penetration performance. It has been stated by Grunewald and Walraven [1] that the incorporation of steel ﬁbres could expand the possible application scope of SCC. In addition, increasing the ﬁbre volume fraction has been proven to improve the mechanical properties of SCC [2, 3] . The strength prediction of normal vibrated concrete (NVC) can be achieved with higher precision via numerical analysis. However, the compositions of SFRSCC are more complex because of the properties of ﬁbres and mineral admixtures, and the traditional methods are not easily applicable to predict its strength. In recent years, machine learning (ML) predictions have been gradually used for evaluating some properties of concrete in the construction industry [4–7] . Many studies have been conducted to predict the mechanical properties of concrete based on machine le

FAQs

What mechanical properties are predicted for SFRSCC in this study?

The models predict flexural strength and compressive strength of SFRSCC specimens.

Which machine learning algorithms are used to build the prediction models?

Support vector regression (SVR) and artificial neural networks (ANN) are employed to create the prediction models.

How is the dataset constructed and prepared for training?

Data are collected from 15 published papers, totaling 161 groups. The dataset is limited to SCC reinforced with 2D hooked-end steel fibres and organized with nine input variables and two output variables.

Aug 3, 2026

Upload by Himbo