Highway construction materials play a critical role in determining the durability, strength, and long-term performance of road infrastructure. The selection of appropriate materials impacts not only the structural integrity of highways but also their maintenance costs and lifespan. However, there is often a challenge in determining which material offers optimal performance under varying environmental and load conditions. This study evaluates the performance of two widely used highway construction materials - Asphalt Concrete (AC) and Portland Cement Concrete (PCC), by analyzing their strength characteristics through data visualization and regression analysis. The research employs a sample comprising 500 data points, where strength scores indicating material performance were recorded for highways constructed with either AC or PCC. Standard data preprocessing techniques, including outlier detection and normalization, were applied to ensure data integrity. Simple Linear Regression (SLR) analysis was utilized to determine the statistical significance of material selection on highway performance. Results indicate that PCC consistently outperforms AC in terms of strength, with an average strength score of 85.6 compared to 73.2 for AC. The regression model yielded an R² value of 0.78, indicating a strong predictive relationship between material type and strength. The F-statistic of 112.45 (p < 0.001) confirms the statistical significance of material choice, reinforcing PCC's superior load-bearing capacity. The heatmap and regression plots further highlight the correlation, while the scatter plot visually demonstrates the spread of strength scores, with PCC clustering at higher values. The study concludes that PCC is the preferred material for high-load highways due to its superior strength and durability, while AC remains viable for flexible pavement applications where rapid installation and cost-effectiveness are key factors. It is recommended that future studies incorporate additional parameter such as material aging effects to refine the performance evaluation model. These findings provide valuable insights for civil engineers and policymakers in optimizing material selection for sustainable and high-performance highway infrastructure. In future improvement of this work, we suggest the inclusion of performance indicators, such as durability under varying environmental conditions, cost analysis, or service life predictions for a more robust model.
Okon et al. (Sat,) studied this question.