Location
San Diego, California
Presentation Date
27 May 2010, 4:30 pm - 6:20 pm
Abstract
Though various mechanistic based models are there for prediction of lateral displacement of ground, the statistical/ empirical methods based on the insitu data is most widely used. The artificial neural network method has been extensively used an alternate statistical method in different complex geotechnical engineering problems. Due to inherent difficulty of generalization in artificial neural network method, support vector machine, which is based on statistical learning algorithm is also being used. This paper describes use of extreme learning machine for prediction of large lateral displacement of liquefaction induced ground during an earthquake. Extreme learning machine is an artificial intelligence techniques based on artificial neural network and has been explored here as an alternate statistical method. The results so obtained have been compared with the results obtained using artificial neural network and support vector machine.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
5th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics
Publisher
Missouri University of Science and Technology
Document Version
Final Version
Rights
© 2010 Missouri University of Science and Technology, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Document Type
Article - Conference proceedings
File Type
text
Language
English
Recommended Citation
Das, Sarat Kumar, "Prediction of Lateral Displacement of Liquefaction Induced Ground Using Extreme Learning" (2010). International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. 31.
https://scholarsmine.mst.edu/icrageesd/05icrageesd/session04/31
Included in
Prediction of Lateral Displacement of Liquefaction Induced Ground Using Extreme Learning
San Diego, California
Though various mechanistic based models are there for prediction of lateral displacement of ground, the statistical/ empirical methods based on the insitu data is most widely used. The artificial neural network method has been extensively used an alternate statistical method in different complex geotechnical engineering problems. Due to inherent difficulty of generalization in artificial neural network method, support vector machine, which is based on statistical learning algorithm is also being used. This paper describes use of extreme learning machine for prediction of large lateral displacement of liquefaction induced ground during an earthquake. Extreme learning machine is an artificial intelligence techniques based on artificial neural network and has been explored here as an alternate statistical method. The results so obtained have been compared with the results obtained using artificial neural network and support vector machine.
