Aminu Suleiman

Rising temperatures and shifting precipitation patterns driven by anthropogenic climate change accelerate flexible pavement deterioration in climate-sensitive regions. Despite the growing vulnerability of the semiarid zones in West Africa, regional studies remain limited. This study presents, to the best of our knowledge, the first mechanistic–empirical assessment of climate change impacts on Nigeria’s semiarid pavements by integrating downscaled NASA NEX-GDDP-CMIP6 climate projections (2020–2080) with MnPAVE modeling using four recommended ensemble global climate models under four shared socioeconomic pathway (SSP) scenarios. Even under the current climatic conditions, the analyzed pavement structure shows design life deficits of 10.2% for fatigue and 53.0% for rutting. Future projections worsen these trends: during the 2020s–2040s design period, fatigue life shortfalls increase by 3.3% to 20.4% across SSP1-2.6 to SSP5-8.5; these widen to 8.7% to 38.3% in the 2050s–2070s maintenance period. Similarly, rutting deficits increased from 2.4% to 13.4% during the design phase and up to 5.0%–25.0% during maintenance. The study attributed more than 80% of fatigue failures to dry-season heat and more than 60% of rutting to wet-season moisture and thermal softening. A novel pavement climate sensitivity catalog was developed to optimize hot-mix asphalt (HMA) thicknesses, which were evaluated across four crushed stone base thickness options under varying temperature and precipitation conditions using Monte Carlo simulations. The results show that increasing the crushed stone base thickness from 200 to 350 mm while reducing the HMA thickness from 167 to 79 mm significantly enhances climate resilience, with only a 4% cost increase compared to a 46% increase for thicker asphalt designs. Thus, optimizing the base-asphalt ratio significantly improves sustainability under climate change. The study concluded that sustainable road infrastructure requires integrating climate projections into pavement design, implementing cost-effective layer adjustments, and adopting climate-resilient guidelines.

This study investigates the impacts of feature engineering techniques, including Clustering, Target Encoding and Anomaly Detection, in conjunction with data balancing methods, on the efficacy of machine learning models for predicting road accident severity. Automated Machine Learning (AutoML), Distributed Random Forest (DRF), Boosted Regression Trees (BRT) and Deep Learning models were evaluated on datasets that were balanced using the SMOTE (Synthetic Minority Over-Sampling Technique) and ADASYN (Adaptive Synthetic Sampling) techniques. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Log Loss, Area under the Curve (AUC), and Area under the Precision-Recall Curve (AUCPR) are employed. Results reveal that the AutoML consistently outperforms other models, achieving an 85% accuracy in predicting fatal accidents and 94% accuracy in predicting injuries. Deep Learning excels in injury accident prediction, with a 95% accuracy, but faces challenges with fatalities, achieving a 60% accuracy. The study underscores the critical role of feature engineering techniques and data balancing methods in enhancing predictive accuracy for accident severity classification. Specifically, the incorporation of Clustering, Target Encoding and Anomaly Detection techniques alongside SMOTE and ADASYN balancing methods significantly improves the model performance. Further refinement and validation are crucial for optimising model performance in real-world traffic safety management applications.