Wang, Zhi-FengCheng, Wen-Chieh2022-04-262022-04-262021-062096-2754http://hdl.handle.net/10919/109749This paper describes an approach for predicting the diameter of a jet-grout column using the support vector regression (SVR) technique, which is regarded as a novel learning machine based upon recent advances in statistical theory, in which the combined effects of the construction (construction methods and jetting parameters) and soil properties (soil type and shearing resistance) are considered. Four different kernel functions, namely, a linear kernel function, polynomial kernel function, radial basis kernel function, and sigmoid kernel function, are integrated into the SVR technique. A large amount of field measured data on the diameter of jet-grout column are retrieved from the published literature for training and testing purposes. The results indicate that the SVR technique with a radial basis kernel function provides predictions closest to the measured results, whereas the prepared design charts enable the ability to significantly widen the application of the proposed approach to the areas of ground improvement and environmental protection.application/pdfenCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 InternationalJet groutingSupport vector regressionMachine learningRadial basis functionArticle - Refereedhttps://doi.org/10.1016/j.undsp.2020.02.004632467-9674