Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization
Samuel Olamide Aregbesola,
Yong‐Hoon Byun
Abstract: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 … Show more
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