An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine. (10th April 2014)

• Record Type: Journal Article
• Authors: Tran, Hong-Hai; Hoang, Nhat-Duc
• Other Names: Lui Eric Academic Editor.
• Is Part Of: Journal of construction engineering. Volume 2014(2014)
• Publication Date: 2014-04-10
• Subjects: Construction projects -- Periodicals; Civil engineering -- Periodicals; Engineering -- Periodicals; Construction industry -- Periodicals; Civil engineering; Construction industry; Construction projects; Engineering; Electronic journals; Periodicals; 624
• DOI: 10.1155/2014/109184
• Languages: English
• ISSNs: 2356-7295

Abstract

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.