PoYOSSClaySen: A framework for post-yield oedometer stress-strain prediction of sensitive clays

Abstract

Accurate prediction of soil settlements is one of the most fundamental tasks in geotechnical engineering. Traditionally, it is based on soil sampling and oedometer tests in the laboratory, which require considerable time and financial resources. In addition, it determines settlement properties at individual local points, which may well be a source of uncertainty, especially in heterogeneous soils. This study presents a novel mathematical approach to predict the oedometer post-yield stress-strain, σv′−εa, behaviour of sensitive clays sampled in southern Finland. The proposed model comprises five sub-models: a) data collection, b) data pre-processing, c) deformation moduli determination, d) learning-based predictor, and e) conflict-resolution Game theory models. In the data collection and data pre-processing sub-models, results from constant-rate-of-strain (CRS) oedometer tests conducted for five testing sites, were compiled and processed to refine minor imperfections. The deformation constrained moduli were then approximated by developing a multi-objective optimization model coupled with the Game theory Condorcet’s social choice rule (SCR) model. As the learning-based predictor, artificial neural network (ANN) models were trained to predict the post-yield σv′−εa behaviour, and the best performing model was identified through the Game theory Condorcet’s SCR conflict-resolution model. The results indicated that the ANN model trained by the Bayesian Regularization (BR) method, using natural water content, wn, soil sensitivity, St, and yield stress, σy′, predicted the deformation curves most accurately. Further evaluation of different ANN training methods and input combinations, using the Game theory bargaining Borda scoring method, showed that wn and St together can be the best combination of soil properties to predict the post-yield σv′−εa curves almost accurately.