Efficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study implements a set of machine learning algorithms to building material science, which predict the compressive strength of zeolite and diatomite substituting concrete mixes in sodium chloride solution. Particularly, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting algorithms are exploited and their optimal parameters are tuned. In the training and testing of these models, 28 day, 56 day, and 90 day compressive strength observations of 63 samples of 7 different concrete mixtures substituting Portland cement, zeolite, diatomite, zeolite + diatomite were used. Consequently, compressive strength experimentation results and machine learning predictions were compared through statistical methods such as RMSE, MAPE, and R 2. Results denote that the prediction performance of machine learning is improving with tuned models. Particularly, RMSE, MAPE, R 2 scores of Categorical Boosting are, respectively, 1.15, 1.45%, and 98.03% after parameter tuning design. The results denote that presented machine learning model can provide an advantage in the cost and duration of the compressive strength experiments.
Açıklama
Anahtar Kelimeler
Zeolite, Diatomite, Compressive strength, Random forest, Gradient boosting, Machine learning
Kaynak
Arabian Journal For Science and Engineering
WoS Q Değeri
N/A
