An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine
Joint Authors
Source
Journal of Construction Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Permeation grouting is a commonly used approach for soil improvement in construction engineering.
Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project.
In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts.
In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and failure.
Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter.
The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting.
An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.
American Psychological Association (APA)
Tran, Hong-Hai& Hoang, Nhat-Duc. 2014. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine. Journal of Construction Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-447130
Modern Language Association (MLA)
Tran, Hong-Hai& Hoang, Nhat-Duc. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine. Journal of Construction Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-447130
American Medical Association (AMA)
Tran, Hong-Hai& Hoang, Nhat-Duc. An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine. Journal of Construction Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-447130
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-447130