Feasibility of an evolutionary artificial intelligence (AI) scheme for modelling of load settlement response of concrete piles embedded in cohesionless soil

• Published in: Ships and offshore structures Vol. 13; no. 7; pp. 705 - 718
• Main Authors: Jebur, Ameer A., Atherton, William, Al Khaddar, Rafid M.
• Format: Journal Article
• Language: English
• Published: Cambridge Taylor & Francis 03.10.2018; Taylor & Francis Ltd
• Subjects: Artificial intelligence; Aspect ratio; Bearing strength; Carrying capacity; Concrete; Concrete piles; Evolutionary algorithms; Feasibility studies; Levenberg-Marquardt MATLAB algorithm; Load carrying capacity; Mathematical models; Modelling; Pile friction; Pile load tests; pile load-settlement; Pile settlement; pile slenderness' ratio; Piles; Ratios; Rigidity; sand relative density; sandy soil; Sandy soils; Soil; Soil investigations; Statistical analysis; Statistical methods; Tests

This investigation aimed to examine the load carrying capacity of piles embedded in sandy soil of various densities, and to develop a predictive model to determine pile settlement using a novel artificial intelligence (AI) method. Experimental pile load tests were conducted using three concrete pile...