Tensile Strength Prediction Model for Expansive Soils Based on Stacked Generalization

CHEN Yang; WANG Lei; LI Tianyi

Abstract：Accurate assessment of the tensile strength of soils is an important issue in geotechnical engineering practice.In this paper,a series of tests were carried out to obtain 125 sets of test data containing information on dry density,moisture content,unconfined compressive strength,uniaxial tensile strength,matrix suction,destructive compressive strain,destructive tensile strain,tensile shear cohesion,and tensile shear angle of internal friction,etc..On this basis,the stacked generalization algorithm is used to build the uniaxial tensile strength prediction model for compacted expansive soils,and the prediction performance of the stacked generalization model is thoroughly compared with other machine learning models and theoretical models of tensile strength based on suction stress.Finally,feature importance analysis is performed to investigate how well the stack generalized model captures the relationship between tensile strength and other variables.The results show that the proposed model outperforms all the other models involved.The major influential variables recognized by the proposed model are matrix suction(44.6%)>moisture content(19.5%)>tensile-shear internal friction angle(11.9%)>unconfined compressive strength(10.6%)>failure tensile strength(8.0%),which is consistent with the results of established studies.