التفاصيل البيبلوغرافية
| العنوان: |
Neural network prediction of the reliability of heterogeneous cohesive slopes |
| المؤلفون: |
William S. Kaggwa, D. V. Griffiths, Mark B. Jaksa, Y. Chok, Gordon A. Fenton |
| المصدر: |
International Journal for Numerical and Analytical Methods in Geomechanics. 40:1556-1569 |
| بيانات النشر: |
Wiley, 2016. |
| سنة النشر: |
2016 |
| مصطلحات موضوعية: |
Random field, Artificial neural network, business.industry, Computer science, Monte Carlo method, 0211 other engineering and technologies, Computational Mechanics, 02 engineering and technology, Geotechnical Engineering and Engineering Geology, Machine learning, computer.software_genre, Perceptron, Finite element method, Probabilistic method, Mechanics of Materials, 021105 building & construction, General Materials Science, Artificial intelligence, business, Slope stability analysis, computer, Algorithm, Reliability (statistics), 021101 geological & geomatics engineering |
| الوصف: |
Summary The reliability of heterogeneous slopes can be evaluated using a wide range of available probabilistic methods. One of these methods is the random finite element method (RFEM), which combines random field theory with the non-linear elasto-plastic finite element slope stability analysis method. The RFEM computes the probability of failure of a slope using the Monte Carlo simulation process. The major drawback of this approach is the intensive computational time required, mainly due to the finite element analysis and the Monte Carlo simulation process. Therefore, a simplified model or solution, which can bypass the computationally intensive and time-consuming numerical analyses, is desirable. The present study investigates the feasibility of using artificial neural networks (ANNs) to develop such a simplified model. ANNs are well known for their strong capability in mapping the input and output relationship of complex non-linear systems. The RFEM is used to generate possible solutions and to establish a large database that is used to develop and verify the ANN model. In this paper, multi-layer perceptrons, which are trained with the back-propagation algorithm, are used. The results of various performance measures indicate that the developed ANN model has a high degree of accuracy in predicting the reliability of heterogeneous slopes. The developed ANN model is then transformed into relatively simple formulae for direct application in practice. Copyright © 2016 John Wiley & Sons, Ltd. |
| تدمد: |
0363-9061 |
| DOI: |
10.1002/nag.2496 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_________::5dc88b2d9cd35ef6080396e8ee5b045b
https://doi.org/10.1002/nag.2496 |
| Rights: |
CLOSED |
| رقم الانضمام: |
edsair.doi...........5dc88b2d9cd35ef6080396e8ee5b045b |
| قاعدة البيانات: |
OpenAIRE |