Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and？？failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance. 1. Introduction In construction engineering, permeation grouting is the process that involves the injection of suitable particulate grouts or chemical solutions into the geomaterial with the aim of improving its mechanical properties and reducing the water movement through soils [1]. In particular for underground construction works, the inflow of groundwater has always been a substantial challenge for geotechnical engineers [2]. Water inflows often cause construction delays and severe damages to the structure quality. Consequently, the grouting activity is an essential task which needs to be performed in a majority of underground construction projects. Recently, microfine cement grouts have been increasingly employed by geotechnical engineers. The reason is that microfine cement grouts can provide an improved groutability for the target geomaterial and they do not contaminate the surrounding environment. In addition, these grouts are proven to have the capacity of filling cracks with small openings as well as penetrating fine soils with very low permeability [3]. Nonetheless, one of the main challenges in the utilization of microfine cement grouts is how to accurately estimate the groutability of the target geomaterial [4]. It is because the grouting process is based on the complex time-dependent transport process of cement grains through the soil matrix. Moreover, besides the grain size of the soil and the grout, other factors that affect the outcome of grouting
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