The use of self-organizing feature map networks for the prediction of the critical factor of safety of an artificial slope

Title:

The use of self-organizing feature map networks for the prediction of the critical factor of safety of an artificial slope

Creator:

Erzin , Y., Nikoo, M., and Cetin , T.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:14936e3b-5b57-4798-84a3-b308b798417d
uuid:14936e3b-5b57-4798-84a3-b308b798417d
doi:10.14311/NNW.2016.26.027

Subject:

critical factor of safety, earthquake forces, pseudo-statistic approach, Self Organization Feature Map, and simplified Bishop method

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this study, the performance of three different self organization feature map (SOFM) network models denoted as SOFM1, SOFM2, and SOFM3 having neighborhood shapes, namely, SquareKohonenful, LineKohonenful, and Diamond-Kohenenful, respectively, to predict the critical factor of safety (Fs) of a widely-used artificial slope subjected to earthquake forces was investigated and compared. For this purpose, the reported data sets by Erzin and Cetin (2012) [7], including the minimum (critical) Fs values of the artificial slope calculated by using the simplified Bishop method, were utilized in the development of the SOFM models. The results obtained from the SOFM models were compared with those obtained from the calculations. It is found that the SOFM1 model exhibits more reliable predictions than SOFM2 and SOFM3 models. Moreover, the performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed to evaluate the prediction capacity of the SOFM models developed. The study demonstrates that the SOFM1 model is able to predict the Fs value of the artificial slope, quite efficiently, and is superior to the SOFM2 and SOFM3.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:5

Harvested from:

CDK

Metadata only:

false