The prediction of the grouting volume is a very important task in the grouting quality control. Because of the concealment and complexity of the karst curtain grouting engineering, there is little research on the prediction of the karst curtain grouting volume (KCGV), and the prediction is hindered by the practical problems of small samples, high dimensions, and nonlinearity. In the study, based on the basic idea of support vector machine (SVM), a multiparameter comprehensive intelligent prediction method of the KCGV is proposed, which overcomes the limitation of few sample data in practical engineering. This method takes the grouting construction conditions and the slurry conditions which control the slurry diffusion as the input parameters, which are the basic data which can be easily obtained in the field grouting process. This feature greatly improves the prediction accuracy and generalization performance of the method. The intelligent prediction method of the KCGV based on SVM is applied to a typical karst curtain grouting project. The mean absolute error of the prediction results is 3.47 L/m, and the mean absolute percentage error of the prediction results is 5.97%. The results show that the proposed prediction method has an excellent prediction effect on the KCGV and can provide practical and beneficial help for the field karst curtain grouting project.

中文翻译：

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基于支持向量机的岩溶帷幕注浆量智能预测方法

注浆量的预测是注浆质量控制中一项非常重要的任务。由于岩溶帷幕注浆工程的隐蔽性和复杂性，对岩溶帷幕注浆量（KCGV）预测的研究较少，且由于样本小、维数高、非线性等实际问题阻碍了预测。研究中，基于支持向量机（SVM）的基本思想，提出了一种KCGV的多参数综合智能预测方法，克服了实际工程中样本数据少的局限性。该方法以灌浆施工条件和控制泥浆扩散的泥浆条件为输入参数，是现场灌浆过程中容易获得的基础数据。该特性极大地提高了该方法的预测精度和泛化性能。将基于SVM的KCGV智能预测方法应用于典型的岩溶帷幕注浆工程。预测结果的平均绝对误差为3.47 L/m，预测结果的平均绝对百分比误差为5.97%。结果表明，所提出的预测方法对KCGV具有良好的预测效果，可为野外岩溶帷幕注浆工程提供实际有益的帮助。预测结果的平均绝对百分比误差为5.97%。结果表明，所提出的预测方法对KCGV具有良好的预测效果，可为野外岩溶帷幕注浆工程提供实际有益的帮助。预测结果的平均绝对百分比误差为5.97%。结果表明，所提出的预测方法对KCGV具有良好的预测效果，可为野外岩溶帷幕注浆工程提供实际有益的帮助。