Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time
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- @Article{khanmohammadi:2022:Mathematics,
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author = "Mohammadreza Khanmohammadi and
Danial Jahed Armaghani and Mohanad Muayad {Sabri Sabri}",
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title = "Prediction and Optimization of Pile Bearing Capacity
Considering Effects of Time",
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journal = "Mathematics",
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year = "2022",
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volume = "10",
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number = "19",
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pages = "Article No. 3563",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2227-7390",
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URL = "https://www.mdpi.com/2227-7390/10/19/3563",
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DOI = "doi:10.3390/math10193563",
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abstract = "Prediction of pile bearing capacity has been
considered an unsolved problem for years. This study
presents a practical solution for the preparation and
maximization of pile bearing capacity, considering the
effects of time after the end of pile driving. The
prediction phase proposes an intelligent equation using
a genetic programming (GP) model. Thus, pile geometry,
soil properties, initial pile capacity, and time after
the end of driving were considered predictors to
predict pile bearing capacity. The developed GP
equation provided an acceptable level of accuracy in
estimating pile bearing capacity. In the optimisation
phase, the developed GP equation was used as input in
two powerful optimisation algorithms, namely, the
artificial bee colony (ABC) and the grey wolf
optimisation (GWO), in order to obtain the highest
bearing capacity of the pile, which corresponds to the
optimum values for input parameters. Among these two
algorithms, GWO obtained a higher value for pile
capacity compared to the ABC algorithm. The introduced
models and their modelling procedure in this study can
be used to predict the ultimate capacity of piles in
such projects.",
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notes = "also known as \cite{math10193563}",
- }
Genetic Programming entries for
Mohammadreza Khanmohammadi
Danial Jahed Armaghani
Mohanad Muayad Sabri Sabri
Citations