Neural Network Approach for Fatigue Crack Prediction in Asphalt Pavements Using Falling Weight Deflectometer Data

Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fatigue cracking more effectively than traditional methods. A comprehensive dataset from the Long-Term Pavement Performance (LTPP) program is used for model training and validation. The ANN’s ability to adapt and recognize patterns enhances its predictive accuracy, allowing for more reliable pavement condition assessments. Model performance is evaluated against real-world data, confirming its effectiveness in predicting fatigue cracking with an overall R2 of 0.9. This study’s findings provide valuable insights for pavement maintenance and rehabilitation planning, helping transportation agencies optimize repair schedules and reduce costs. This research highlights the growing role of AI in pavement engineering, demonstrating how machine learning can improve infrastructure management. By integrating ANN-based predictive analytics, road agencies can enhance decision-making, leading to more durable and cost-effective pavement systems for the future.