Volume 26, Issue 2 (ُSummer 2022)                   jwss 2022, 26(2): 119-134 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Seifollahi M, Abbasi S, Lotfollahi-yaghin M, Daneshfaraz R, Kalateh F, Fahimi-Farzam M. Investigation of the Performance of Artificial Intelligence Methods in Estimating the Crest Settlement of Rockfill Dam with a Central Core. jwss 2022; 26 (2) :119-134
URL: http://jstnar.iut.ac.ir/article-1-4140-en.html
University of Maragheh , daneshfaraz@yahoo.com
Abstract:   (1518 Views)
Unpredictable settlement of earth dams has led researchers to develop new methods such as artificial neural networks, wavelet theory, fuzzy logic, and a combination of them. These methods do not require time-consuming analyses for estimation. In this research, the amount of settlement in rockfill dams with a central core has been estimated using artificial intelligence methods. The data of 35 rockfill dams with a central core were used to train and validate the models. The artificial neural network, wavelet transform model, and fuzzy-neural adaptive inference system are the proposed models which were used in the present study. According to the results, the best model for an artificial neural network had two hidden layers, the first layer of 18 neurons and the second layer of 7 neurons, with the Tansig-Tansig activation function, with a coefficient of determination R2=0.4969. The best model for the fuzzy-neural inference system had the ring function (Dsigmoid) as a membership function, with three membership functions and 142 repetitions with a coefficient of determination R2=0.2860. Also, combining wavelet-neural network conversion with the coif2 wavelet function due to the more adaptation this function has to the input variables, the better the performance, and this function, with a coefficient of determination R2=0.9447, had the highest accuracy compared to other models.
Full-Text [PDF 968 kb]   (825 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2021/02/26 | Accepted: 2021/08/14 | Published: 2022/09/1

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb