Activities per year
Project Details
Grant Program
Faculty Development Competitive Research Grant Program 2021-2023
Project Description
Mechanized excavation refers to method of rock breakage where the rock is entirely removed from face by the action of mechanical boring/cutting tools. The excavation method has become increasingly common, especially in underground mining and civil construction. Mechanized rock excavation is traditionally compared to drill and blast operation. The characteristic advantages of mechanical excavation are increased safety, higher production rate, less labor intensive operation, higher degree of automation, minimum damage to the walls, better control over the process, uniformity of product size, elimination of blast vibrations, and better ventilation in underground openings. The most common types of mechanized excavators are tunnel boring machines (TBMs), roadheader, raise boring machine (RBM), continuous miner, and longwall drum shear to be used for either civil or mining construction.
Status | Finished |
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Effective start/end date | 1/1/21 → 12/31/23 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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TBM tunnelling in adverse rock mass and geological conditions
Saffet Yagiz (Invited speaker)
Dec 18 2023Activity: Talk or presentation › Invited talk
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National Technical University of Athens
Saffet Yagiz (Visiting researcher)
Nov 8 2023 → Nov 10 2023Activity: Visiting types › Visiting an external research institution
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Colorado School of Mines
Saffet Yagiz (Visiting researcher)
Dec 2 2023 → Dec 13 2023Activity: Visiting types › Visiting an external research institution
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Estimating the penetration rate of tunnel boring machines via gradient boosting algorithms
Ghorbani, E. & Yagiz, S., Oct 2024, In: Engineering Applications of Artificial Intelligence. 136, 108985.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Performance analysis of drilling machines based on rock properties and machine’s specifications
Yazitova, A. & Yagiz, S., Jan 2024, In: Bulletin of Engineering Geology and the Environment. 83, 1, 37.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Predicting disc cutter wear using two optimized machine learning techniques
Ghorbani, E. & Yagiz, S., Apr 2024, In: Archives of Civil and Mechanical Engineering. 24, 106.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus)