Techno Press
You logged in as Techno Press

Geomechanics and Engineering
  Volume 22, Number 5, September10 2020 , pages 397-414
DOI: https://doi.org/10.12989/gae.2020.22.5.397
 


Indirect measure of shear strength parameters of fiber-reinforced sandy soil using laboratory tests and intelligent systems
Danial Jahed Armaghani, Fatemeh Mirzaei, Ali Toghroli and Ali Shariati

 
Abstract
    In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.
 
Key Words
    shear strength; reinforced-soil; artificial neural network; neuro-imperialism
 
Address
Danial Jahed Armaghani: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

Fatemeh Mirzaei: Department of Civil Engineering, Bu-Ali Sina University, Hamedan, Iran

Ali Toghroli: Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Ali Shariati: 1.) Division of Computational Mathematics and Engineering, Institute for Computational Science,
Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
2.) Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2023 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com