Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time

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@Article{khanmohammadi:2022:Mathematics,

• author = "Mohammadreza Khanmohammadi and Danial Jahed Armaghani and Mohanad Muayad {Sabri Sabri}",
• title = "Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time",
• journal = "Mathematics",
• year = "2022",
• volume = "10",
• number = "19",
• pages = "Article No. 3563",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-7390",
• URL = "https://www.mdpi.com/2227-7390/10/19/3563",
• DOI = "doi:10.3390/math10193563",
• 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.",
• notes = "also known as \cite{math10193563}",

}

Genetic Programming entries for Mohammadreza Khanmohammadi Danial Jahed Armaghani Mohanad Muayad Sabri Sabri

Citations