Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.

中文翻译：

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通过机器学习对动态锥体阻力进行基于信号的自动评估，以进行地下表征


动态锥阻力（DCR）是最近推出的土壤阻力指数，其单位为应力。它是根据仪器化动态锥入度计尖端的动态响应确定的。然而，DCR 评估通常是一个手动、耗时且容易出错的过程。因此，本研究调查了使用堆叠集成 (SE) 机器学习 (ML) 模型确定 DCR 的可行性，该模型利用动态锥入度测试获得的信号。两项 ML 实验表明，仅使用力信号可以更准确地预测 DCR。此外，SE 技术在这两种情况下都优于基础学习算法。总体而言，研究结果表明机器学习技术可用于自动分析力和加速度信号的 DCR。