Effects of Heavy Agricultural Vehicle Loading on Pavement Performance

Abstract

Agricultural equipment manufacturers have been producing equipment with larger capacity to meet the demands of today’s agricultural industry. This rapid shift in equipment size has raised concerns within the pavement industry, as these heavy vehicles have potential to cause significant pavement damage. At present, all implements of husbandry are exempted from axle weight and gross vehicle weight restrictions in Minnesota. However, they must comply with the 500 lb per inch of tire width restriction which may lead to very large loads as long as the tires are sufficiently wide. A full scale accelerated pavement test was conducted at the MnROAD test facility. Both flexible and rigid pavements were tested in this study. This thesis presented analysis performed on the flexible pavement sections. The flexible pavement sections consisted of a “thin section” which represented a typical 7-ton road and a “thick section” which represented a 10-ton road. Both sections were instrumented with strain gages, earth pressure cells, and LVDTs to measure pavement responses generated by these heavy agricultural vehicles. These response measurements were compared to responses generated by a typical 5-axle semi truck. Additionally, tire contact area and contact stresses of these vehicles were measured. Through this research, it was determined that traffic wander, seasonal changes, time of testing, pavement structure, and gross vehicle weight have profound effects on pavement response measurements. The effect of vehicle speed and benefits of flotation tires over radial ply tires were not significant in this study. Additionally, all agricultural vehicles loaded above 80% of full capacity generated higher subgrade stresses compared to the 80-kip 5-axle semi truck. Layered elastic programs, BISAR and MnLayer were used in the modeling analysis. The contact areas of these vehicles were approximated through multi-circular area estimation. This detailed modeling of the contact area yielded a more realistic representation of the actual vehicle footprint. DAKOTA-MnLayer optimization framework was introduced to perform backcalculation analysis to determine Young’s moduli of the pavement layers. The backcalculated Young’s moduli resulted in a close match between predicted responses and field measurements.