Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques


Open Access

Year : 2025 | Volume : 13 | Special Issue 01 | Page : 963-973
    By

    Mayank Nigam,

  • Manvendra Verma,

  1. Assistant Professor, Department of Civil Engineering, GLA University, Mathura, Uttar Pradesh, India
  2. Assistant Professor, Department of Civil Engineering, GLA University,Mathura, Uttar Pradesh, India

Abstract

Nano-silica, or nanosilica, refers to silicon dioxide nanoparticles, which are a kind of silica (SiO₂) with diameters that often fall below 100 nanometers. This nanomaterial has attracted considerable attention because of its distinctive characteristics and diverse array of uses, notably in augmenting the performance of materials such as concrete. The integration of nanoparticles with cementitious matrix in nano-silica concrete offers a viable approach to improving the mechanical characteristics and longevity of concrete constructions. The use of machine learning (ML) techniques to forecast the strength properties of nano-silica concrete is investigated in this work. Machine learning (ML) methods provide a data-driven method for properly forecasting the compressive strength, flexural strength, and other pertinent mechanical parameters by making use of the inherent complexity of nanomaterial interactions inside the concrete matrix. Compiling a large dataset with different mix designs, nano-silica concentrations, curing settings, and testing parameters is the research’s main task. After the dataset’s significant features are extracted using feature engineering approaches, the model is subjected to validation, testing, and training phases. Regression models, support vector machines, artificial neural networks, and other machine learning techniques are tested to see which method works best for strength prediction. The impact of various hyperparameters and feature selection techniques on the models’ prediction performance is also examined in this study. The findings show that machine learning (ML) based models show promise in precisely forecasting the strength characteristics of nano-silica concrete. This provides important information for mix design optimisation and improving the overall performance of concrete structures in construction applications.

Keywords: Concrete composites; Mechanical properties; Nano-silica; Artificial Neural Network; Machine Learning Techniques

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

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How to cite this article:
Mayank Nigam, Manvendra Verma. Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques. Journal of Polymer and Composites. 2024; 13(01):963-973.
How to cite this URL:
Mayank Nigam, Manvendra Verma. Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques. Journal of Polymer and Composites. 2024; 13(01):963-973. Available from: https://journals.stmjournals.com/jopc/article=2024/view=190110



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Special Issue Open Access Original Research
Volume 13
Special Issue 01
Received 23/04/2024
Accepted 20/08/2024
Published 18/12/2024
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