Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm

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.

Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm

Cited by 4 publications

References 90 publications

Optimizing Seismic Earth Pressure Estimates for Battered Retaining Walls Using Numerical Methods and ANN

Optimizing Seismic Earth Pressure Estimates for Battered Retaining Walls Using Numerical Methods and ANN

Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model

Shallow-buried subway station construction period: Comparison of intelligent early warning and optimization strategies for surface deformation risk

Contact Info

Product

Resources

About