Developing an advanced ANN-based approach to estimate compaction characteristics of highway subgrade

The current methodology of assessing the compaction characteristic of subgrade fillers is time-consuming. The spot-sampling method and the following laboratory tests bring bias to engineering practice. This study proposes an advanced approach to determine the maximum dry density, optimum moisture content, and compactness of subgrade based on ANN algorithms. A large number of compaction tests are performed to assess the compaction quality of various types of subgrade fillers. The particle gradation is specifically considered. Wave propagations of the compacted specimen are assessed based on the ultrasonic test, and the anisotropic feature is demonstrated. The dynamic elastic parameters are derived to further reveal the interaction mechanism. A dataset is proposed by combining these laboratory data and the literature data. The PSO-BP-NN model is developed to automatically predict the compaction parameters of different filling materials. A positive correlation is demonstrated between the elastic modulus and the total density, and Poisson's ratio illustrates an inverse trend. The compaction energy is influential to the maximum dry density while the moisture content has the greatest impact on the compactness. This study aims to develop an accurate model to replace the extensive Proctor test and efficiently evaluate the compaction quality of subgrade for highway constructions.

Automated summary

The current methodology of assessing the compaction characteristic of subgrade fillers is time-consuming and biased. This study proposes an advanced approach based on ANN algorithms to determine compaction parameters of subgrade fillers. A large number of compaction tests are performed to assess the compaction quality and the particle gradation is specifically considered. The developed PSO-BP-NN model can automatically predict the compaction parameters of different filling materials.