Machine learning enabled closed‐form models to predict strength of alkali‐activated systems-Reference-Cited by-同舟云学术

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Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

Published:2022-02-15 Issue:6 Volume:105 Page:4414-4425
ISSN:0002-7820
Container-title:Journal of the American Ceramic Society
language:en
Short-container-title:J Am Ceram Soc.

Author:

Han Taihao¹^ORCID,Gomaa Eslam²,Gheni Ahmed²,Huang Jie³,ElGawady Mohamed²,Kumar Aditya¹^ORCID

Affiliation:

1. Department of Materials Science and Engineering Missouri University of Science and Technology Rolla Missouri USA

2. Department of Civil, Architectural, and Environmental Engineering Missouri University of Science and Technology Rolla Missouri USA

3. Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri USA

Funder

National Science Foundation

Leonard Wood Institute

Ameren Corporation

Publisher

Wiley

Subject

Materials Chemistry,Ceramics and Composites

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jace.18399

Reference77 articles.

1. Geopolymers and Related Alkali-Activated Materials

2. One-part alkali-activated materials: A review

3. Geopolymerisation: A review and prospects for the minerals industry

4. Repair of ordinary Portland cement concrete using ambient-cured alkali-activated concrete: Interfacial behavior

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1. Prediction of time-dependent concrete mechanical properties based on advanced deep learning models considering complex variables;Case Studies in Construction Materials;2024-12

2. 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

3. Effects of Rest Time and Curing Regime on Short- and Long-Term Strength of Class C Fly Ash-Based Alkali-Activated Mortars;Materials;2024-04-02

4. Prediction of Time-Dependent Concrete Mechanical Properties Based on Advanced Deep Learning Models Considering Complex Variables;2024

5. Can domain knowledge benefit machine learning for concrete property prediction?;Journal of the American Ceramic Society;2023-11-10

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