Pile/shaft designs using artificial neural networks (i.e., genetic programming) with spatial variability considerations.
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
Clear All

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

  • 2014-03-01

Filetype[PDF-1.11 MB]

  • English

  • Details:

    • Publication/ Report Number:
    • Resource Type:
    • Geographical Coverage:
    • Abstract:
      The work focused on the improvement of FB-DEEP’s prediction of skin and tip resistance of concrete piles and drilled shafts in Florida. For the work, data from 19 concrete pile sites and 18 drilled shaft sites were collected. This included 458 standard penetration test, SPT, borings on the pile sites and 815 borings on the drilled shaft sites. A total of 64 static pile load tests and 66 drilled shaft tests were acquired. For the piles, 48 tests reached Davisson Capacity, of which 28 had separation of skin and tip resistance. All of the drilled shafts were instrumented with strain gauges from which unit skin transfer (T–Z) was assessed for Florida limestone. All of the data were uploaded into the FDOT online database based on position (i.e., station + offset, or GPS). In the case of piles, the data (e.g., boring vs. measured skin friction) were analyzed with a genetic program (GP) algorithm to construct equations for unit skin friction and tip resistance based on soil type (USCS) and SPT N values. The resulting GP skin friction curves were found to be similar to FB-DEEP; the tip resistance curves had higher unit tip resistance vs. blow count values, as well as being only averaged 4 diameters/widths beneath the piles. In addition, the practice of setting SPT N to zero for N< 5 was found to be conservative, and the use of N=5 for N< 5 is recommended. For both current FB-DEEP and GP curves, load resistance factor design, LRFD , were obtained for borings within 100 ft. In the case of borings outside this distance or for sitespecific conditions, method error (CVm) for FB-DEEP and the GP curves is presented from which LRFD  may be found. In the case of drilled shaft, the GP algorithm a developed normalized unit skin friction vs. displacement curve for limestone, which were similar to Kim (2001). In the case of ultimate skin friction in limestone, the GP algorithm was used to validate the FDOT relationship between unit skin friction and rock strength (unconfined compression, split tension).
    • Format:
    • Main Document Checksum:
    • File Type:

    Supporting Files

    • No Additional Files

    More +

    You May Also Like

    Checkout today's featured content at rosap.ntl.bts.gov

    Version 3.16