Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques, but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects. The high dispersion in the properties of the improved material leads to designers assuming a conservative, arbitrary and unjustified strength, which is even sometimes subjected to the results of the test fields. The present paper presents an approach for prediction of the uniaxial compressive strength (UCS) of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers. The selected machine learning model (extremely randomized trees) relates the soil type and various parameters of the technique to the value of the compressive strength. Despite the complex mechanism that surrounds the jet grouting process, evidenced by the high dispersion and low correlation of the variables studied, the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works. Consequently, this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.