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Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

机译：支持向量机在旋喷桩单轴抗压强度预测中的应用

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摘要

Learning from data is a very attractive alternative to "manually" learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.
机译：从数据中学习是“手动”学习的一种非常有吸引力的选择。因此，在过去的十年中，机器学习的使用已迅速遍及整个计算机科学及其他领域。这种在高级统计分析上得到支持的方法通常称为数据挖掘（DM），并已成功应用于不同的知识领域。在本研究中，我们表明DM可以为解决土木工程（即岩土工程领域）中的复杂问题做出巨大贡献。特别是，将支持向量机（SVM）算法的高学习能力（其具有灵活性和非线性能力）表征为预测直接从JG柱中提取的喷射灌浆（JG）样品的单轴抗压强度（UCS）的方法，通常称为土坯。 JG技术是一种世界范围内广泛应用的软土改良方法，与其他方法相比具有极强的通用性和经济吸引力。但是，即使经过多年的经验，JG色谱柱设计仍缺乏准确的方法。因此，在本文中，基于从不同岩土工程中收集到的472个结果，提出了一种基于SVM算法的UCS预测混凝土混合物的新方法。此外，应用全局敏感性分析是为了从所提出的模型中解释和提取可理解的知识。这种分析使人们能够确定UCS预测中的关键变量并测量其影响。最后，提出并讨论了根据实验室研究发展UCS预测的初步步骤。

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