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

McVay, Michael; University of Florida

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

2014-03-01
By McVay, Michael

[PDF-1015.13 KB]

English

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Details:

Creators:

McVay, Michael
Corporate Creators:

University of Florida
Subject/TRT Terms:

[+]

Bridge Foundations Data Collection Genetic Algorithms Limestone Load Tests Piles (Supports) Prestressed Concrete Skin Resistance 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.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:48a06f8169eb0575c00479c5d104f35d4407351dbf52d89d00c8a2eb138df395
Download URL:

https://rosap.ntl.bts.gov/view/dot/27186/dot_27186_DS1.pdf
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Pile/shaft designs using artificial neural networks (i.e., genetic programming) with spatial variability considerations : [summary].

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