Table of Contents
Journal of Construction Engineering
Volume 2014 (2014), Article ID 109184, 9 pages
http://dx.doi.org/10.1155/2014/109184
Research Article

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

Department of Technology and Construction Management, Faculty of Building and Industrial Construction, National University of Civil Engineering, Room 307, A1 Building, No. 55 Giai Phong Road, Hanoi 10000, Vietnam

Received 23 December 2013; Revised 27 February 2014; Accepted 20 March 2014; Published 10 April 2014

Academic Editor: Eric Lui

Copyright © 2014 Hong-Hai Tran and Nhat-Duc Hoang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.