Machine learning-based compressive strength estimation in nano silica-modified concrete

Mahsa Farshbaf Maherian*, Servan Baran, Sidar Nihat Bicakci, Behcet Ugur Toreyin, Hakan Nuri Atahan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

This study investigated the efficacy of advanced machine learning (ML) algorithms for predicting the compressive strength (CS) of concrete modified with nano-silica and supplementary cementitious materials. Utilizing datasets with 1143 samples with a CS rage of 4–129 MPa derived from established experimental literature, the predictive performance of these models was quantitatively evaluated via statistical measures. The outcomes revealed that the Random Forest (R2 = 0.93) and Artificial Neural Networks (R2 = 0.92) models excelled in accuracy, indicating the potential of ML techniques to enhance mixture designs, thus providing substantial savings in both time and fiscal resources related to experimental evaluations.

Original languageEnglish
Article number133684
JournalConstruction and Building Materials
Volume408
DOIs
Publication statusPublished - 8 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Artificial neural networks
  • Compressive strength
  • Concrete
  • Machine learning
  • Nano-silica
  • Support vector machines

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