An investigation of pile design utilizing advanced data analytics

Nikolaos P. Machairas, Magued G. Iskander

Research output: Contribution to journalConference articlepeer-review


This study explores the use of state-of-the-art data analytics techniques for predicting the axial load capacity of piles. A support vector machine algorithm was developed. About 213 load tests obtained from FHWA's deep foundation load test database (DFLTD) version 2 were used to evaluate the performance of the developed approach against the FHWA design method. The scope was limited to impact-driven, un-tapered, steel, and concrete piles, loaded in compression, using a static load test. The results of the predictive analysis show an improvement over the capacities obtained by the FHWA pile design method. Perhaps more remarkably, the predictive model outperformed the FHWA pile design method by relying only on seven readily available features as compared to a laborious and error-prone design methodology. This study demonstrates the potential of machine learning in geotechnical engineering as an alternative to conventional design approaches. The methodology is also demonstrated with an online capacity computation tool.

Original languageEnglish (US)
Pages (from-to)132-141
Number of pages10
JournalGeotechnical Special Publication
Issue numberGSP 294
StatePublished - 2018
Event3rd International Foundations Congress and Equipment Expo 2018: Installation, Testing, and Analysis of Deep Foundations, IFCEE 2018 - Orlando, United States
Duration: Mar 5 2018Mar 10 2018

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Building and Construction
  • Geotechnical Engineering and Engineering Geology


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