The deterioration of pavement infrastructure represents a critical challenge for transportation agencies worldwide, with climate variability significantly accelerating the degradation process and increasing maintenance costs. Traditional pavement performance models have proven inadequate in capturing the complex, non-linear relationships between environmental factors and pavement deterioration patterns, particularly under increasingly variable climate conditions (Omisola et al., 2020). This research introduces a comprehensive deep learning framework for predicting pavement deterioration that incorporates multiple climate variables including temperature fluctuations, precipitation patterns, freeze-thaw cycles, and humidity variations.
The study develops and validates several deep learning architectures including Long Short-Term Memory networks, Convolutional Neural Networks, and hybrid models to analyze pavement condition data collected from various climate zones across North America. The research methodology combines historical pavement performance data spanning fifteen years with high-resolution climate datasets to train predictive models capable of forecasting pavement deterioration under different climate scenarios. Feature engineering techniques are employed to extract meaningful patterns from raw climate data, while advanced preprocessing methods ensure data quality and consistency across multiple data sources.
Results demonstrate that the proposed deep learning models achieve superior prediction accuracy compared to traditional empirical models, with mean absolute error reductions of up to forty-two percent for International Roughness Index predictions and thirty-seven percent for Pavement Condition Index forecasting. The models successfully capture the accelerated deterioration effects of extreme weather events, including heat waves, severe freeze-thaw cycles, and prolonged wet periods. Sensitivity analysis reveals that temperature variability and freeze-thaw frequency are the most significant climate factors affecting pavement deterioration rates across different pavement types and age categories.
The research findings provide valuable insights for pavement management systems, enabling more accurate budget forecasting and optimized maintenance scheduling under changing climate conditions. The developed models demonstrate robust performance across diverse geographic regions and climate zones, suggesting broad applicability for transportation agencies seeking to improve infrastructure resilience. Implementation of these predictive models can support proactive maintenance strategies, reduce lifecycle costs, and enhance the sustainability of transportation infrastructure in the face of climate change.