Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization
Author(s): |
Samuel Olamide Aregbesola
Yong‐Hoon Byun |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Computer-Aided Civil and Infrastructure Engineering, August 2024, n. 16, v. 39 |
Page(s): | 2541-2552 |
DOI: | 10.1111/mice.13294 |
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 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. |
- About this
data sheet - Reference-ID
10791647 - Published on:
01/09/2024 - Last updated on:
01/09/2024