Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches

Abstract The use of cement as a curing agent has been widely adopted in soft soil engineering to increase the strength of soft soil. The cemented soil is gradually exposed to the air and in a natural environment becomes unsaturated. Unconfined compressive strength (UCS) of the unsaturated cemented soils is a key parameter for assessing their strength behaviour. UCS determination of unsaturated cemented soils by using laboratory methods is a complex, time-consuming, and expensive procedure due to the difficulty in suction control. This study aims to model the UCS of unsaturated cemented Wenzhou clay, i.e., capture the nonlinear relations between UCS and its influential variables including cement content (%), dry density (g/cm3) and suction (MPa) for the first time by using machine learning approach. Toward this aim, three advanced computational frameworks are developed based on hybrid evolutionary approaches in which evolutionary optimisation algorithms including genetic algorithm (GA), particle swarm optimisation (PSO) and imperialist competitive algorithm (ICA) are hybridised with artificial neural network (ANN). Results show that developed models have a great ability to mimic the nonlinear relationships between UCS and its influential variables and PSO-ANN presents the best performance among three models on the training dataset with R 2 = 0.9888 , RMSE = 0.129 and VAF = 97.742 , and testing dataset with R 2 = 0.9412 , RMSE = 0.237 and VAF = 90.414 . To facilitate engineering application, an engineering database for Wenzhou soft clay at different cement ratios (up to 11%), suctions (up to 300 MPa) and dry densities (1–1.5 g/cm3) is built by using the developed PSO-ANN model.