An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete-Reference-Cited by-同舟云学术

An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete

Published:2020-05 Issue: Volume:244 Page:118271
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Han Taihao^ORCID,Siddique Ashfia,Khayat Kamal^ORCID,Huang Jie^ORCID,Kumar Aditya^ORCID

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference81 articles.

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2. O. US EPA, Resource Conservation and Recovery Act (RCRA) in Focus: Hazardous Waste Generator Guidance by Industry, US EPA. (2015). https://www.epa.gov/hwgenerators/resource-conservation-and-recovery-act-rcra-focus-hazardous-waste-generator-guidance (accessed 26.04.19).

3. O. US EPA, Sustainable Management of Construction and Demolition Materials, US EPA. (2016). https://www.epa.gov/smm/sustainable-management-construction-and-demolition-materials (accessed 02.05.19).

4. Cracking susceptibility of concrete made with recycled concrete;Isgor;Aggregate,2013

5. Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm;Behnood;Constr. Build. Mater.,2015

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