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

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    • 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).

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