Experimental investigation and comparative machine learning prediction of the compressive strength of recycled aggregate concrete incorporated with fly ash, GGBS, and metakaolin

Biswal, Uma Shankar; Mishra, Mayank; Singh, Manav Kumar; Pasla, Dinakar

doi:10.1007/s41062-022-00844-6

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Experimental investigation and comparative machine learning prediction of the compressive strength of recycled aggregate concrete incorporated with fly ash, GGBS, and metakaolin

Cited by 20 publications

References 76 publications

Mechanical Performance of 100% Recycled Aggregate Based Geopolymer Concrete at Various Concentrations of NaOH

Mechanical Performance of 100% Recycled Aggregate Based Geopolymer Concrete at Various Concentrations of NaOH

Predictive Modelling of Flexural Strength in Recycled Aggregate-Based Concrete: A Comprehensive Approach with Machine Learning and Global Sensitivity Analysis

Amelioration of sandwich panels by replacing polyurethane foam with coconut husk and study on computational prediction using ANN and LR

Contact Info

Product

Resources

About