Shear Strength Prediction of Treated Soft Clay with Sugarcane Bagasse Ash Using Artificial Intelligence Methods
-
Published:2023-05-30
Issue:3
Volume:35
Page:597-605
-
ISSN:2289-7526
-
Container-title:Jurnal Kejuruteraan
-
language:
-
Short-container-title:jkukm
Author:
Che Mamat Rufaizal, ,Ramli Azuin,P. Rosyidi Sri Atmaja, ,
Abstract
Soil shear strength is an essential engineering characteristic used in designing and evaluating geotechnical structures. In this study, we intend to analyse and compare the performance of the Genetic Algorithm - Adaptive Network-based Fuzzy Inference System (GANFIS) and Artificial Neural Networks (ANN) in predicting the strength of soft clay. Case studies of 144 soft clay soil samples from Sarang Buaya, Semerah, Malaysia, were utilised to generate training and testing datasets for developing and validating models. RMSE and R have been employed to validate and compare the models. The GANFIS has the highest prediction capability (RMSE=0.042 and R=0.850), while the ANN has the lowest (RMSE=0.065 and R=0.49). From a comparison of the two models, it can be stated that GANFIS is the most promising technique for predicting the strength of soft clay.
Publisher
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献