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

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7 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Makale numarası133684
DergiConstruction and Building Materials
Hacim408
DOI'lar
Yayın durumuYayınlandı - 8 Ara 2023

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© 2023 Elsevier Ltd

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