A data-driven approach to the strength evaluation of airfield rigid pavement using in situ instrumentation data

Abstract

The pavement strength evaluation plays a vital role in the airfield operation and management. Traditionally, the evaluation has relied on the Heavy Weight Deflectometer (HWD) test. This method encounters challenges, including interruptions in airfield operations, limited coverage of inspection locations, extensive time required for data collection and analysis. This paper introduces a methodology for evaluating the structural strength of airfield rigid pavement using instrumentation data, aiming to enhance both the efficiency and accuracy of such evaluations. Strain responses, collected using the instrumentation system at Chengdu Tianfu International Airport under HWD loading, are extensively analyzed. A three-dimensional numerical simulation model encompassing nine slabs was formulated, leveraging finite element analysis to replicate pavement working conditions that were not covered by the instrumentation samples. The merging of the instrumentation and simulation samples resulted in a comprehensive database for diverse pavement working conditions. This facilitated the establishment of a data-driven framework for the fusion of mechanical models. From this database, a neural network mapping model was developed to correlate the instrumentation data with the pavement slab modulus and subgrade reaction modulus. These correlations were subsequently employed for the calculation of the pavement classification number (PCN). The primary advantages of the proposed method include real-time monitoring and evaluation of airfield rigid pavement strength without interference to airfield operations. The developed method has been deployed at seven hub airports in China, encompassing Shanghai Pudong International Airport (PVG), Beijing Capital International Airport (PEK), and Chengdu Tianfu International Airport (TFU), etc.