Development of a Cost Oriented Grinding Strategy and Prediction of Post Grind Roughness using Improved Grinder Models

Abstract

Irregularities in pavement profiles that exceed standard thresholds are usually rectified using a Diamond Grinding Process. Diamond Grinding is a method of Concrete Pavement Rehabilitation that involves the use of grinding wheels mounted on a machine that scraps off the top surface of the pavement to smooth irregularities. Profile Analysis Software like ProVAL© offers simulation modules that allow users to investigate various grinding strategies and prepare a corrective action plan for the pavement. The major drawback with the current Smoothness Assurance Module© (SAM) in ProVAL© is that it provides numerous grind locations which are both redundant and not feasible in the field. This problem can be overcome by providing a constrained grinding model in which a cost function is minimized; the resulting grinding strategy satisfies requirements at the least possible cost. Another drawback with SAM exists in the built-in grinder models that do not factor in the effect of speed and depth of cut on the grinding head. High speeds or deep cuts will result in the grinding head riding out the cut and likely worsening the roughness. A constrained grinding strategy algorithm with grinder models that factor in speed and depth of cut that results in cost effective grinding with better prediction of post grind surfaces through simulation is developed in this work. The outcome of the developed algorithm is compared to ProVAL's© SAM results.