Adaptation of Crumb Rubber Modified Asphalt Predictive Models for Nigerian Climatic Conditions: A Transfer Learning Approach

Crumb Rubber Modified Asphalt (CRMA) represents a major advancement in sustainable road construction, widely adopted in the United States to improve pavement durability, reduce rutting, and utilize waste tires. However, its application in developing countries like Nigeria remains limited, largely due to the lack of region-specific performance models, climatic differences, and infrastructural challenges. This study proposes a transfer learning approach to adapt predictive CRMA models from the United States to Nigerian climatic zones using climate matching, multivariate regression, artificial neural networks (ANN), and multi-objective optimization techniques. Using simulated data representative of U.S. state climates and traffic conditions, we modeled performance indices such as Marshall Stability, rutting resistance, and fatigue retention. The results identify optimal crumb rubber contents (CR%) of 10–15% for different climate-traffic scenarios. Enhanced models including traffic loads (ESALs) were developed and mapped to Nigerian conditions. This supports sustainable CRMA deployment for road infrastructure in Nigeria and similar regions.

crumb rubber, predictive modeling, asphalt performance, optimization, Nigeria, ANN, regression

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