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Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms

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International RILEM Conference on Synergising Expertise towards Sustainability and Robustness of Cement-based Materials and Concrete Structures (SynerCrete 2023)

Part of the book series: RILEM Bookseries ((RILEM,volume 44))

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Abstract

With the development of big data processing technology and the continuous improvement of computer 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, fly ash), 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.

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Authors and Affiliations

  1. Cardiff University, Cardiff, CF24 3AA, UK

    Tianyi Cui, Sivakumar Kulasegaram & Haijiang Li

Authors
  1. Tianyi Cui
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  2. Sivakumar Kulasegaram
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  3. Haijiang Li
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Corresponding author

Correspondence to Tianyi Cui .

Editor information

Editors and Affiliations

  1. Department of Structural Engineering, Silesian University of Technology, Gliwice, Poland

    Agnieszka Jędrzejewska

  2. Technical Specialist Services, Materials, ARUP, London, UK

    Fragkoulis Kanavaris

  3. ISISE, University of Minho, Guimaraes, Portugal

    Miguel Azenha

  4. Laboratoire de Mécanique Paris-Saclay, ENS Paris-Saclay, Gif-sur-Yvette, France

    Farid Benboudjema

  5. Institute of Structural Concrete, Graz University of Technology, Graz, Austria

    Dirk Schlicke

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Cui, T., Kulasegaram, S., Li, H. (2023). Prediction of Mechanical Properties of Steel Fibre-Reinforced Self-compacting Concrete by Machine Learning Algorithms. In: Jędrzejewska, A., Kanavaris, F., Azenha, M., Benboudjema, F., Schlicke, D. (eds) International RILEM Conference on Synergising Expertise towards Sustainability and Robustness of Cement-based Materials and Concrete Structures. SynerCrete 2023. RILEM Bookseries, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-031-33187-9_65

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  • DOI: https://doi.org/10.1007/978-3-031-33187-9_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33186-2

  • Online ISBN: 978-3-031-33187-9

  • eBook Packages: EngineeringEngineering (R0)

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