Abstract

Asphalt pavements deteriorate over time when subjected to fatigue; hence, it is critical to evaluate in-service pavements to keep them in good condition. The goal of pavement evaluation is to assess the functional and structural performance of a pavement section, with a view to improving both the pavement's serviceability and riding quality. Through field and laboratory experiments, the current study attempts to evaluate the performance of pavement stretches. The sections are ranked and prioritized based on the test results. Medchal-Dabilpur national highway was evaluated for pavement roughness, skid resistance and deflections. In addition, core samples with diameters of 100mm and 150mm were collected and tested in the laboratory for moisture susceptibility, tensile strength, stiffness and fracture characteristics. The current study also intends to establish a unique technique for prioritizing pavement maintenance sections based on functional and structural performance in the field and laboratory. The Artificial Neural Network (ANN) approach was used to develop the relationship between pavement functional and structural performance. Heat maps were created and ranked in Python Jupyter notebook to better visualize the performance of functional and structural characteristics of the pavement. The final evaluation results can be validated by supporting them with laboratory investigations based on the pavement-section field samples. The results demonstrate that there is a strong relationship between the structural and functional properties of the pavement. The developed Maintenance Priority Index (MPI) will be useful in rating the maintenance and rehabilitation actions depending on the level of necessity. KEYWORDS: Functional and structural evaluation, Resilient modulus, Tensile strength ratio, Fracture properties, ANN technique, Maintenance priority index.

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