Development of a Temperature-Sensitive Modulus Model for Flexible Pavements Based on Regional Climatic and Traffic Variations

In order to manage the resource used in road construction and for decreasing costs to optimum levels, maintenance and repair needs should be determined correctly and required actions should be applied at the right time. The current structural and functional condition of the pavement must be evaluated for management of the pavement of the road network. Current condition can be calculated by methods determined by using non-destructive testing methods with Falling Weight Deflectometers (FWD). But in this method, the test results of asphalt effected by asphalt temperature.Different climate types are seen in different regions in Turkey and hot-cold temperature differences are more common compared to other countries. Therefore, it would not be appropriate to design the pavement with a single reference temperature. In this study, reference temperatures for different regions were calculated based on meteorological data, depending on traffic volumes.Also, in this study, the temperature effect of Hot Mix Asphalt (HMA) layers on deflectometer measurements of Flexible Pavements were investigated and a layer modulus temperature correction method was developed for bituminous HMA layers in the light of field studies conducted.This article discusses the process of updating a temperature-dependent linear elasticity modulus model using new experimental data. A model based on elasticity modulus values obtained from experiments conducted at various temperatures was initially established, and the study explores how this model can be updated with new experimental data. The update process involves defining a tolerance range around the temperature at which the new experiment was conducted. The elasticity modulus values from previous experiments that fall within this range are averaged to adjust the trend line of the model. The impact of different tolerance ranges on the trend line is examined, showing that a narrow tolerance range leads to minimal changes, while a wider range can cause significant shifts in the trend line. This method ensures that the model remains robust and adaptable to new data while accounting for variability in measurements.