Pile/shaft designs using artificial neural networks (i.e., genetic programming) with spatial variability considerations : [summary].

Details

• Creators:
  McVay, Michael
• Corporate Creators:
  University of Florida
• Subject/TRT Terms:
  • Bridge foundations
  • Piles (Supports)
  • Skin resistance
  • Data collection
  • Load tests
  • Prestressed concrete
  • Genetic algorithms
  • Limestone
  • Strain gages
• Publication/ Report Number:
  BDK75-977-68
• Resource Type:
  Brief
• Geographical Coverage:
  Florida
• Corporate Publisher:
  Florida. Dept. of Transportation
• Abstract:
  In this project, University of Florida researchers ; sought to improve the unit skin friction and tip ; resistance correlations embedded in the FB-Deep ; software algorithm for estimating driven pile and ; drilled shaft resistance. They utilized an artificial ; neural networks (ANN) approach, shown to reduce ; uncertainty in design. Recently, ANN has been ; coupled with genetic programming (GP), making ; use of so-called genetic evolution concepts to ; develop equations that result in the minimum ; mean square error between the predicted and ; measured values.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/27186/dot_27186_DS1.pdf
• File Type:
  [PDF - 1015.13 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:48a06f8169eb0575c00479c5d104f35d4407351dbf52d89d00c8a2eb138df395