Principal component analysis-artificial neural network-based model for predicting the static strength of seasonally frozen soils.

ABSTRACT: Seasonally frozen soils are exposed to freeze‒thaw cycles every year, leading to mechanical property deterioration. To reasonably describe the deterioration of soil under different conditions, machine learning (ML) technology is used to establish a prediction model for soil static strength. Six key influencing factors (moisture content, compaction degree, confining pressure, freezing temperature, number of freeze‒thaw cycles and thawing duration) are included in the modelling database. The accuracy of three typical ML algorithms (support vector machine (SVM), random forest (RF) and artificial neural network (ANN)) is compared. The results show that the ANN outperforms the SVM and RF. Principal component analysis (PCA) is combined with the ANN, and the PCA-ANN algorithm is proposed, which f

SUBMITTER: Sun Y 

PROVIDER: S-EPMC10522631 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature