Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm

被引：7
|
作者：
Mamat, Rufaizal Che [1 ]
Ramli, Azuin [1 ]
Yazid, Muhamad Razuhanafi Mat [2 ]
Kasa, Anuar [2 ]
Razali, Siti Fatin Mohd [2 ]
Bastam, Mukhlis Nahriri [3 ]
机构：
[1] Politekn Ungku Omar, Dept Civil Engn, Jalan Raja Musa Mahadi, Ipoh 31400, Perak, Malaysia
[2] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Civil Engn, Bangi 43600, Selangor, Malaysia
[3] Univ Bina Darma, Dept Civil Engn, JL Jendral A Yani 3 Palembang, Palembang 30264, South Sumatra, Indonesia
来源：
JURNAL KEJURUTERAAN | 2022年 / 34卷 / 01期
关键词：
Prediction; Road embankment; Slope stability; Safety factor; Artificial neural networks; SAFETY;
D O I：
10.17576/jkukm-2022-34(1)-16
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The prediction of slope stability was performed using artificial neural networks (ANNs) in this work. The factor of safety determined by numerical analysis was used to develop ANN's data sets. The inputs to the network are slope height, applied surcharge and slope angle. Correlation coefficients between numerical data and ANNs outputs showed the feasibility of ANNs for successfully modelling and predicting safety issues. The ANNs training phase is improved using a genetic algorithm (GA), and the results are compared to those obtained without GA trained ANNs. A sensitivity analysis is conducted to ascertain the relative contribution of different factors on slope stability. The slope angle and applied surcharge have a significant effect on slope stability.
引用
收藏
页码：165 / 173
页数：9
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