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Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm

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Abstract

A proper and reliable estimation of bearing capacity of thin-walled foundations is of importance and necessary for accurate design of these structures. This study proposes a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)–polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS–PNN–GA, for prediction of bearing capacity of the thin-walled foundations. In fact, in ANFIS–PNN–GA system, GA was used to optimize the ANFIS–PNN structure. To achieve the aim of this study, a series of data samples were collected from literature. After establishing the database, many ANFIS–PNN–GA models were constructed and proposed to estimate the bearing capacity of the aforementioned foundations. To show capability of this advance hybrid model, two pre-developed models i.e., ANFIS and PNN were also built to predict bearing capacity. The performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). The R values of (0.9825, 0.9071, and 0.9928) and (0.8630, 0.7595 and 0.9241) were obtained for training and testing data of the ANFIS, PNN and ANFIS–PNN–GA, models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS–PNN–GA model are more accurate compared to other implemented methods. The proposed advance hybrid model can be introduced as a new and applicable technique for solving problems in field of geotechnics and civil engineering.

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Abbreviations

ANFIS:

Adoptive neuro-fuzzy inference system

BC:

Bearing capacity

ICA:

Imperialist competitive algorithm

PNN:

Polynomial neural network

FS:

Fuzzy system

ANN:

Artificial neural network

TS:

Takagi–Sugeno

GA:

Genetic algorithm

BP:

Back-propagation

MF:

Membership function

GUI:

Graphical user interface

PSO:

Particle swarm optimization

AI:

Artificial intelligence

FIS:

Fuzzy inference system

B :

Footing width

IBS:

Industrialized building system

D10 :

Grain size

D50 :

Mean grain size

Cu:

Coefficient of uniformity

Lw/B:

Thin-wall length to footing width ratio

LVDT:

Linear variable displacement transducer

Qu:

Maximum bearing capacity

φ :

Soil internal friction angle

ϒ :

Soil unit weight

MSE:

Mean square error

R :

Correlation coefficient

RMSE:

Root mean square error

Error StD:

Error-standard deviation

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Authors and Affiliations

  1. Town Planning, Engineering Systems and Networks Department, South Ural State University, Chelyabinsk, Russian Federation

    Danial Jahed Armaghani

  2. Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran

    Hooman Harandizadeh

  3. Faculty of Engineering, Lorestan University, Khorramabad, 6815144316, Iran

    Ehsan Momeni

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Jahed Armaghani, D., Harandizadeh, H. & Momeni, E. Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm. Engineering with Computers 38 (Suppl 5), 4073–4095 (2022). https://doi.org/10.1007/s00366-021-01380-0

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