Straightforward prediction for air-entry value of compacted soils using machine learning algorithms.

The straightforward prediction for the air-entry value of compacted soils is practically useful, but the investigation on this issue is scarce. This study presents three alternative straightforward prediction models for the air-entry value of compacted soils using the representative machine learning algorithms of multi expression programming (MEP), evolutionary polynomial regression (EPR) and random forest (RF). Five known soil properties (i.e. sand content, fines content, plasticity index, initial water content and initial void ratio) are used as input variables. All models are developed based on a large database, covering a wide range of soil classifications. The results show that all the three proposed models are appropriate to predict the air-entry values of different compacted soils, with high prediction accuracies for both the training and the testing data. The monotonicity, the sensitivity and the robustness of the three prediction models are evaluated, showing consistency among different models with a slight difference and providing a strong support for the model feasibility. On the whole, the MEP and the EPR models are recommended for more convenient applications with explicit expression, while higher prediction accuracy may require the RF model although no explicit expression can be derived. • Air-entry value of compacted soils is precited using three machine learning algorithms (MEP, EPR, RF) with high precision. • The correctness and the feasibility of all the three models are verified. • The MEP and the EPR models are recommended for more convenient applications with high prediction precision. [ABSTRACT FROM AUTHOR]