TY - JOUR
T1 - Machine learning-based compressive strength estimation in nano silica-modified concrete
AU - Maherian, Mahsa Farshbaf
AU - Baran, Servan
AU - Bicakci, Sidar Nihat
AU - Toreyin, Behcet Ugur
AU - Atahan, Hakan Nuri
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12/8
Y1 - 2023/12/8
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Compressive strength
KW - Concrete
KW - Machine learning
KW - Nano-silica
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85173623936&partnerID=8YFLogxK
U2 - 10.1016/j.conbuildmat.2023.133684
DO - 10.1016/j.conbuildmat.2023.133684
M3 - Article
AN - SCOPUS:85173623936
SN - 0950-0618
VL - 408
JO - Construction and Building Materials
JF - Construction and Building Materials
M1 - 133684
ER -