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Optimal Tractor-Blade Configuration Planning System for Eco-Economic Dozing
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 Title & Authors
Optimal Tractor-Blade Configuration Planning System for Eco-Economic Dozing
Park, Young-Jun; Kim, Byung-Soo; Lee, Dong-Eun;
 
 Abstract
Identifying the optimal configuration of tractor (i.e., engine type and size) and blade must be preceded for eco-economic dozing operation. Existing experience based configuration practice is lack of scientific rational. It demands to deal with many variables (e.g., job site's geological and topological attributes, temperature and atmospheric pressure, coefficient of traction, tractor and blade motion data, soil and rock properties, blade's engineering dimension, job and management conditions etc.) simultaneously and timely. A database structure for processing the optimal eco-economic dozer configuration is designed and implemented to replace existing experience based practices. On top of the database, a new method that identifies an optimal set of tractor engine(i.e., type and size) and blade is implemented for a standalone dozer operation. The method is coded into MATLAB to facilitate using the method in practice. A case study demonstrates and verifies the system.
 Keywords
Dozer;Productivity;Eco-dozing;Blade;
 Language
Korean
 Cited by
 References
1.
Caterpillar, Inc. (2010). Caterpillar performance handbook, 40th Ed, Caterpillar, Peoria.

2.
Church, H. K. (1981). Excavation handbook. New York, McGraw-Hill.

3.
Day, D. A., & Benjamin, N. B. (1991). Construction equipment guide. John Wiley & Sons.

4.
Harvers, A. J., & Stubbs, W. F., (1971). Handbook of Construction Equipment Maintenance and Repair, McGraw Hill.

5.
Kim, D., Kim, J., Lee, K., Park, C., Song, J., & Kang, D. (2009). Excavator tele-operation system using a human arm. Automation in construction, 18(2), 173-182. crossref(new window)

6.
Nunnally, S. W. (2004). Construction methods and management. Pearson Prentice Hall, New Jersey.

7.
Ok, S. C., & Sinha, S. K. (2006). Construction equipment productivity estimation using artificial neural network model, Construction Management and Economics, 24(10), 1029-1044. crossref(new window)

8.
Peurifoy, R. L., Schexnayder, C. J., & Shapira, A. (2009). Construction Planning, Equipment, and Methods 7th Ed. Mcgraw-Hill.

9.
Qinsen, Y., & Shuren, S. (1994). A soil-tool interaction model for bulldozer blades. Journal of terramechanics, 31(2), 55-65. crossref(new window)