Date of Award

Spring 5-1-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Technology Management

Department

College of Technology

Abstract

Heavy construction equipment owners and managers have few predictive tools that can estimate wear rate of undercarriage track propulsion systems working in various soil types and changing operational conditions. Managing the timely maintenance of these track systems is critical for they represent over half of the non-fuel operating cost of the equipment fleet. Understanding the major influencing factors that impact undercarriage system wear rate can help determine the most economical time to stop a machine for track maintenance thus positively impacting the equipment’s return on investment (ROI). This research analyzed the population of track type dozers in the eastern half of North Carolina, United States of America. This region has markedly different soil types, topography and precipitation amounts making this to be an excellent study canvas. Sand percentage in the soil where the machine is working is thought to be a primary factor influencing the wear rate. In addition, other factors like precipitation, temperature, machine model, machine weight, altitude above sea level, and work type code are also considered and analyzed to determine which of these factors have significance. A regression model is developed that can be used as a predictive model to help manage this high value maintenance wear item. This research is important because the results can assist machine owners in maximizing the life of the undercarriage system in eastern North Carolina and will result in better machine maintenance decisions. In addition, this research can be utilized to accurately bid construction jobs predicting machine operating expense for each specific job site soil makeup.

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