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 piles, with aspect ratios of 12, 17 and 25. Evolutionary Levenberg–Marquardt MATLAB algorithms, enhanced by T-tests and F-tests, were used in this process. According to the statistical analysis and the relative importance study, pile length, applied load, pile flexural rigidity, pile aspect ratio and sand–pile friction angle were found to play a key role in pile settlement. Results revealed that the proposed optimum model algorithm precisely characterized pile settlement. There was close agreement between the experimental and predicted data (Pearson's R = 0.988, P = 6.28 × 10-31) with a relatively insignificant root mean square error of 0.002.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2018-10-03


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Simplified Method for Laterally Loaded Short Piles in Cohesionless Soil

    Aguilar, Victor / Stallings, J. Michael / Anderson, J. Brian et al. | Transportation Research Record | 2019


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

    Jebur, Ameer A. / Atherton, William / Alattar, Zeinab I. et al. | Taylor & Francis Verlag | 2022


    Winkler springs for axial response of suction bucket foundations in cohesionless soil

    Grecu, Sorin / Ibsen, Lars Bo / Barari, Amin | BASE | 2021

    Freier Zugriff