Prosper E. A. Ayawah

About Prosper E. A. Ayawah

Prosper E. A. Ayawah, With an exceptional h-index of 3 and a recent h-index of 3 (since 2020), a distinguished researcher at Missouri University of Science and Technology, specializes in the field of Geomechanics, Geomechanical modeling, Rock excavation.

His recent articles reflect a diverse array of research interests and contributions to the field:

A novel approach for determining cutting geometry for TBM using full-scale laboratory linear rock cutting and PFC3D-based numerical simulations

Stacked generalization for improved prediction of ground vibration from blasting in open-pit mine operations

A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines

Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions ahead of a TBM Using Data-Driven Approach

Hydraulic Shovel Digging Phase Simulation and Force Prediction Using Machine Learning Techniques

Analytical and numerical assessment of a preliminary support design – a case study

Evaluation of excavated surface irregularities and hardness of mechanical excavations and their relationship with excavator performance

Prosper E. A. Ayawah Information

University

Position

Ph.D. Candidate

Citations(all)

55

Citations(since 2020)

55

Cited By

0

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

Google Scholar

Prosper E. A. Ayawah Skills & Research Interests

Geomechanics

Geomechanical modeling

Rock excavation

Top articles of Prosper E. A. Ayawah

A novel approach for determining cutting geometry for TBM using full-scale laboratory linear rock cutting and PFC3D-based numerical simulations

Tunnelling and Underground Space Technology

2024/2/1

Stacked generalization for improved prediction of ground vibration from blasting in open-pit mine operations

Mining, Metallurgy & Exploration

2022/12

A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines

Tunnelling and Underground Space Technology

2022/4

Towards TBM Automation: On-The-Fly Characterization and Classification of Ground Conditions ahead of a TBM Using Data-Driven Approach

Applied Sciences

2021/12/25

Hydraulic Shovel Digging Phase Simulation and Force Prediction Using Machine Learning Techniques

Mining, Metallurgy & Exploration

2021/9/27

Prosper E. A. Ayawah
Prosper E. A. Ayawah

H-Index: 0

Samuel Frimpong
Samuel Frimpong

H-Index: 13

Analytical and numerical assessment of a preliminary support design – a case study

Cogent Engineering

2021/1/11

Prosper E. A. Ayawah
Prosper E. A. Ayawah

H-Index: 0

Hareyani Zabidi
Hareyani Zabidi

H-Index: 6

Evaluation of excavated surface irregularities and hardness of mechanical excavations and their relationship with excavator performance

2021

See List of Professors in Prosper E. A. Ayawah University(Missouri University of Science and Technology)

Co-Authors

academic-engine