Evaluation of Soil Liquefaction Potential Due to Earthquake using Intelligent Classification Algorithm in Orange Software

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Fattahi Hadi; Jiryaee Fateme

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: FERDOWSI CIVIL ENGINEERING Year:2021 | Volume:34 | Issue:3 Page(s): 39-52

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Evaluation of Soil Liquefaction Potential Due to Earthquake using Intelligent Classification Algorithm in Orange Software

Author(s)

Fattahi Hadi | Jiryaee Fateme | Issue Writer Certificate

Keywords

Earthquake

Liquefaction

intelligent classification algorithms

Orange software

Abstract

One of the possible consequences of earthquakes in saturated areas is soil liquefaction and as a result the failure of foundations of buildings, types of infrastructure, bridges and many other disasters. In this study, in order to evaluate the potential of soil liquefaction on 79 samples from China Tangshan Earthquake Database, several intelligent classification models were constructed with the help of Orange software. Therefore, the performance of 5 intelligent classification methods (Logistic Regression, Artificial Neural Network (ANN), Support Vector Machine (SVM), K-fold Nearest Neighbor (KNN) and Random Forest) were compared based on different criteria. The results showed that SVM, ANN and Logistic Regression methods have a high ability to predict soil liquefaction class and among them the Logistic Regression method with AUC index (0.98) was selected as the best method. In addition, the study of the effectiveness of variables using three criteria of Information Gain, Information Gain Ratio and Gini Index, indicates that the variable measured CPT tip resistance is the most effective variable and is the first priority. The variables of cyclic stress ratio and peak acceleration at the ground surfaceare also important features.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans