Evaluation of rheological and mechanical performance of gangue-based cemented backfill material: a novel hybrid machine learning approach

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.

Evaluation of rheological and mechanical performance of gangue-based cemented backfill material: a novel hybrid machine learning approach

Cited by 2 publications

References 57 publications

Comparative prediction performance of the strength of a new type of Ti tailings cemented backfilling body using PSO-RF, SSA-RF, and WOA-RF models

Comparative prediction performance of the strength of a new type of Ti tailings cemented backfilling body using PSO-RF, SSA-RF, and WOA-RF models

Study on preparation and influencing factors of coal gangue fill slurry material

Contact Info

Product

Resources

About