Doctoral Dissertations

Author

Danish Ali

Keywords and Phrases

Deep Learning; Finite Element Analysis; Mining Truck; Neural Network; Surface Mining; Whole Body Vibrations

Abstract

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to capture the dynamic impact force for a multi-pass shovel loading operation. The research further involved the application of artificial intelligence and machine learning for implementing the impact force detection in real time.

Experimental results showed the impact force magnitudes of 571 kN and 422 kN, for the first and second shovel pass, respectively, through an accurate representation of HISLO with continuous flow modelling using FEA-DEM coupled methodology. The novel ‘DeepImpact’ model, showed an exceptional performance, giving an R2, RMSE, and MAE values of 0.9948, 10.750, and 6.33, respectively, during the model validation.

This research was a pioneering effort for advancing knowledge and frontiers in addressing the WBV challenges in deploying heavy mining machinery in safe and healthy large surface mining environments. The smart and intelligent real-time monitoring system from this study, along with process optimization, minimizes the impact force on truck surface, which in turn reduces the level of vibration on the operator, thus leading to a safer and healthier working mining environments"--Abstract, page iii.

Advisor(s)

Frimpong, Samuel

Committee Member(s)

Alagha, Lana Z.
Galecki, Greg
Chandrashekhara, K.
Madria, Sanjay Kumar

Department(s)

Mining Engineering

Degree Name

Ph. D. in Mining Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2021

Pagination

xiv, 175 pages

Note about bibliography

Includes bibliographic references (pages 160-174).

Rights

© 2021 Danish Ali, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11821

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