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Evaluating the performance of asphalt concrete using CT scanning has become an essential area of research due to its potential to revolutionize the way we assess road materials. Traditional methods often require destructive sampling, which can damage infrastructure and offer limited insight into the material's internal structure. In contrast, CT scanning provides a non-destructive, highly detailed analysis of asphalt's internal features, such as air voids, aggregate distribution, and binder coverage, all of which are critical to its durability and performance. Additionally, the ability to create 3D models from CT scans allows for deeper insights into factors like void connectivity and aggregate bonding, which directly affect the lifespan of pavements. By combining CT imaging with advanced data processing techniques, such as deep learning, this research offers more accurate and reliable methods for optimizing asphalt mix designs, ultimately leading to longer-lasting roads, reduced maintenance costs, and more sustainable construction practices.
