Data driven design of alkali-activated concrete using sequential learning-Reference-Cited by-同舟云学术

Data driven design of alkali-activated concrete using sequential learning

Published:2023-09 Issue: Volume:418 Page:138221
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Völker Christoph^ORCID,Moreno Torres Benjami,Rug Tehseen,Firdous Rafia^ORCID,Jan Zia Ghezal Ahmad,Lüders Stefan,Scaffino Horacio Lisdero^ORCID,Höpler Michael,Böhmer Felix,Pfaff Matthias^ORCID,Stephan Dietmar^ORCID,Kruschwitz Sabine

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment,Building and Construction

Reference89 articles.

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4. Energy and CO2 emission assessments of alkali-activated concrete and Ordinary Portland Cement concrete: a comparative analysis of different grades of concrete;Alsalman;Clean. Environ. Syst.,2021

5. An experimental investigation on volcanic ash – based geopolymer concrete;Al-Tais;International Journal of Engineering Technology Science and Research,2018

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1. Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete;Engineering Applications of Artificial Intelligence;2024-10

2. Self-Driving Laboratories for Chemistry and Materials Science;Chemical Reviews;2024-08-13

3. Effects of alkaline activators and other factors on the properties of geopolymer concrete using industrial wastes based on GEP-based models;European Journal of Environmental and Civil Engineering;2024-05-23

4. Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap;Machine Learning: Science and Technology;2023-12-01

5. Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete;Construction and Building Materials;2023-12