Parametric study on global estimation models for compressive strength adopting various machine learning algorithms in concrete

Author:

Mesfin Woldeamanuel MinwuyeORCID,Kim Hyeong-KiORCID

Funder

National Research Foundation of Korea

Chosun University

Ministry of Education

Publisher

Elsevier BV

Reference51 articles.

1. Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm;Ahmad;Materials,2021

2. Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature;Ahmad;Materials,2021

3. A visual analytics conceptual framework for explorable and steerable partial dependence analysis;Angelini;IEEE Trans. Visual. Comput. Graph.,2023

4. Compressive strength of natural hydraulic lime mortars using soft computing techniques;Apostolopoulou;Procedia Struct. Integr.,2019

5. Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars;Asteris;Comput. Concr.,2019

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