Insights into Plugging of Pipe Piles Based on Pile Dimensions

Antonio Kodsy, Magued Iskander

Research output: Contribution to journalArticlepeer-review

Abstract

Preliminary identification of plugging of open-ended pipe piles based on their dimensions, ahead of driving, is explored in this study using data analytics. Piles can be unplugged, plugged, or internally plugged, depending on their dimensions, and geotechnical conditions. Plugging of pipe piles influences both pile capacity and driving behavior; however, the classification assumed at the design time does not always manifest during driving, sometimes resulting in driving difficulties. The relationship between pile plugging and pile dimensions was investigated using a dataset of 74 load tests on pipe piles, where geotechnical profiles were also available. An analytics approach borrowed from data science was adopted. First, capacity was computed using four recognized designed methods considering the unplugged, plugged, and internally plugged conditions. Next, the calculated capacities were compared to capacities measured (interpreted) from static load tests. Finally, voting was employed to identify plugging based on the closeness of the computed capacity assumptions to the interpreted capacity. Most piles were found to be unplugged. A diameter criterion is proposed as a tool to give early insight into the plugging condition of a pile ahead of driving which resulted in a 70 ± 10% accuracy. The proposed criterion was validated once using a dataset of 23 piles with CPT data and a second time using 24 published driving records where plugging records were available and achieved similar accuracy, in both cases. It was concluded that piles larger than ~0.9 m (36 inches) in diameter have a higher likelihood of being unplugged, while piles smaller than 0.5 m (20 inches) have a higher likelihood of being plugged.

Original languageEnglish (US)
Article number2711
JournalApplied Sciences (Switzerland)
Volume12
Issue number5
DOIs
StatePublished - Mar 1 2022

Keywords

  • Data analytics
  • Data science
  • Data-driven decisions
  • Deep foundation
  • Diameter
  • L/D ratio
  • Length
  • Resistance

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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