Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. Part 1: Efficient data collection and modeling-Reference-Cited by-同舟云学术

Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. Part 1: Efficient data collection and modeling

Published:2022-06 Issue: Volume:4 Page:100082
ISSN:2772-3976
Container-title:Cleaner Materials
language:en
Short-container-title:Cleaner Materials

Author:

Tavares Cesario,Wang Xincheng,Saha Sajib,Grasley Zachary

Publisher

Elsevier BV

Reference62 articles.

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4. JJ Park, S.K., KT Koh, SW Kim. Influence of the ingredients on the compressive strength of UHPC as a fundamental study to optimize the mixing proportion. in International Symposium Ultra High Performance Concrete (UHPC). 2008. Kassel, Germany.

5. Talebinejad, I.B., S.; Iranmanesh, A.; Shekarchizadeh, M., Optimizing Mix Proportions of Normal Weight Reactive Powder Concrete with Strengths of 200-350 MPa. Proceedings of the International Symposium on UHPC, Kassel, Germany, 2004: p. 133-141.

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