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An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Auteur(s):

Médium: article de revue
Langue(s): en 
Publié dans: Journal of Construction Engineering, , v. 2014
Page(s): 1-9
DOI: 10.1155/2014/109184
Abstrait:

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:successand 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.

Copyright: © 2014 Hong-Hai Tran and Nhat-Duc Hoang.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 3.0 (CC-BY 3.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée.

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  • Reference-ID
    10177353
  • Publié(e) le:
    02.12.2018
  • Modifié(e) le:
    02.06.2021