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Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

Cheng, Min-Yuan and Prayogo, Doddy and Wu, Yu-Wei (2014) Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture. [UNSPECIFIED]

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    Abstract

    An effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is utilized to assist users in performing optimization tasks.

    Item Type: UNSPECIFIED
    Additional Information: Untuk dimasukkan ke repository
    Uncontrolled Keywords: High-performance concrete; Genetic algorithm; Evolutionary support vector machine; Graphic user interface
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    T Technology > TA Engineering (General). Civil engineering (General)
    Divisions: Faculty of Civil Engineering and Planning > Civil Engineering Department
    Depositing User: Admin
    Date Deposited: 18 Jul 2017 16:24
    Last Modified: 01 Sep 2017 01:02
    URI: http://repository.petra.ac.id/id/eprint/17651

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