A new approach for Modelling pile settlement of concrete piles under uplift loading using an evolutionary LM training algorithm

This study was performed to examine pile uplift bearing capacity and to develop a novel approach to simulate pile load-settlement response using a new supervised computational intelligence (CI) approach. To reach the planned aim, a series of experimental studies were conducted on concrete piles subj...

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Bibliographic Details
Published inShips and offshore structures Vol. 17; no. 6; pp. 1413 - 1425
Main Authors Jebur, Ameer A., Atherton, William, Alattar, Zeinab I., Al Khaddar, Rafid M.
Format Journal Article
LanguageEnglish
Published Cambridge Taylor & Francis 03.06.2022
Taylor & Francis Ltd
Subjects
Algorithms
Artificial intelligence
Aspect ratio
Bearing capacity
computational intelligence
Concrete
concrete pile
Concrete piles
Deep foundations
Evolutionary algorithms
Levenberg-Marquardt (LM) algorithm
Mean square errors
Pile bearing capacities
Pile friction
Pile settlement
pile uplift capacity
Piles
Rigidity
Root-mean-square errors
sandy soil
Statistical analysis
Statistical methods
Training
Uplift
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Summary:This study was performed to examine pile uplift bearing capacity and to develop a novel approach to simulate pile load-settlement response using a new supervised computational intelligence (CI) approach. To reach the planned aim, a series of experimental studies were conducted on concrete piles subjected to uplift loading. . According to the statistical analysis, pile effective length (lc), applied load (P), pile flexural rigidity (EA), sand-pile friction angle (δ), and pile aspect ratio (lc/d) were pronounced to play a key role, at different contribution levels, on model output. To evaluate and verify the efficiency of the proposed approach, comparison have been made between the employed training algorithm with experimental pile load-test, and with those specified by a number of design procedures. The results revealed an outstanding agreement between the target and predicted pile-load settlement, thus yielding a coefficient of correlation (R), and a minimum root mean square error (RMSE) of 0.985 and 0.059 respectively, thus, in parallel with a relatively insignificant mean square error level (MSE) of 0.0039.
ISSN:1744-5302
1754-212X
DOI:10.1080/17445302.2021.1926658