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Artificial Intelligence Review

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03.09.2021

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

verfasst von: Danial Jahed Armaghani, Hooman Harandizadeh, Ehsan Momeni, Harnedi Maizir, Jian Zhou

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2022

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Abstract

The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

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Titel: An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity
verfasst von: Danial Jahed Armaghani
Hooman Harandizadeh
Ehsan Momeni
Harnedi Maizir
Jian Zhou
Publikationsdatum: 03.09.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 3/2022
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-10065-5

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