Abstract
Addressing the pressing need for optimized airport road maintenance, this study harnesses a machine-learning approach to predict road service life. This study introduces a novel EO-LightGBM model, refined through meticulous parameter optimization, feature selection, and model integration. The relationship between the characteristics of concrete structures and the service life of airport roads was deeply explored and revealed a significant correlation between concrete structural characteristics—compressive strength, flexural strength, density, moisture content, and temperature resistance—and road longevity. The established model not only accurately predicts the service life of airport roads, but also has good stability and adaptability. This study provides a new method for predicting and optimizing the service life of airport roads and provides effective support for the design, construction, and maintenance of airport roads.
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