Compaction Uniformity Evaluation of Subgrade in Highway Based on Principal Components Analysis and Back Propagation Neural Networks

This paper proposes a comprehensive method for the compaction uniformity evaluation of subgrade in highways based on the principle components analysis and BP neural network. A field test on resilient and Young’s moduli of subgrade during compaction is performed on Zun-Qin highway. The moduli representing the compaction uniformity are the key factors in the principal component analysis, and the components are used as input in Back Propagation (BP) neural networks. The degree of variation and synthesis score of moduli in three subgrade sections are discussed, and the results show that the comprehensive method has a good performance in evaluating the compaction uniformity of the subgrade. The insight from this study provides a novel evaluation method and incites a better understanding of the compaction uniformity of subgrade in highways.