This study was motivated by the difficulty in determining the resilient modulus of soils using the repeated load triaxial test (RLTT) recommended by the mechanistic-empirical pavement design guide (MEPDG). An alternative means to estimate the resilient modulus of fine-grained soils has been established in the form of three models that were developed using three supervised machine-learning techniques. The benefits of this model is its application for regions where access to high quality testing is limited or non-existent, and there is a strong need for adequate estimates/proxies. This work was an international collaboration between Prof. Nweke and Dr. Chijioke Ikeagwuani and Prof. Hyginua Onah, Nigerian Researchers from the University of Nigeria, Nsukka Department of Civil Engineering.
The paper can be accessed here.