A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete

Min-Yuan Cheng () and Nhat-Duc Hoang ()
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Min-Yuan Cheng: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Section 4, Keelung Road, Daan District, Taipei 106, Taiwan
Nhat-Duc Hoang: Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809 - K7/25 Quang Trung, Danang 55000, Vietnam

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 03, 603-619

Abstract: This paper presents an AI approach named as self-Adaptive fuzzy least squares support vector machines inference model (SFLSIM) for predicting compressive strength of rubberized concrete. The SFLSIM consists of a fuzzification process for converting crisp input data into membership grades and an inference engine which is constructed based on least squares support vector machines (LS-SVM). Moreover, the proposed inference model integrates differential evolution (DE) to adaptively search for the most appropriate profiles of fuzzy membership functions (MFs) as well as the LS-SVM’s tuning parameters. In this study, 70 concrete mix samples are utilized to train and test the SFLSIM. According to experimental results, the SFLSIM can achieve a comparatively low MAPE which is less than 2%.

Keywords: Rubberized concrete; strength estimate; fuzzy logic; least squares support vector machines; differential evolution (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0219622016500140

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