Permeation grouting is a commonly used approach for soil improvement in construction engineering.Thus, predicting the results of\ngrouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel\nartificial intelligence approachââ?¬â?auto tuning support vector machineââ?¬â?is proposed to forecast the result of grouting activities that\nemploy micro fine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting\nactivities into two classes: success and failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to\nidentify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter.\nThe integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior\nknowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly\nestablished method can produce an outstanding prediction performance.
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