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    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/32216


    標題: A Computer-Aided Approach to Pozzolanic Concrete Mix Design
    作者: Kao, Ching-Yun
    Shen, Chin-Hung
    Jan, Jing-Chi
    Hung, Shih-Lin
    貢獻者: Chia Nan Univ Pharm & Sci, Dept Appl Geoinformat
    Natl Chiao Tung Univ, Dept Civil Engn
    Chien Hsin Univ Sci & Technol, Dept Comp Sci & Informat Engn
    關鍵字: Artificial Neural-Networks
    Self-Compacting Concrete
    High-Strength Concrete
    Blast-Furnace Slag
    Fly-Ash Concrete
    Silica Fume
    Mechanical-Properties
    Compressive Strength
    Structural Damage
    Performance
    日期: 2018
    上傳時間: 2019-11-15 15:45:32 (UTC+8)
    出版者: HINDAWI LTD
    摘要: Pozzolanic concrete has superior properties, such as high strength and workability. The precise proportioning and modeling of the concrete mixture are important when considering its applications. There have been many efforts to develop computer-aided approaches for pozzolanic concrete mix design, such as artificial neural network- (ANN-) based approaches, but these approaches have proven to be somewhat difficult in practical engineering applications. This study develops a two-step computer-aided approach for pozzolanic concrete mix design. The first step is establishing a dataset of pozzolanic concrete mixture proportioning which conforms to American Concrete Institute code, consisting of experimental data collected from the literature as well as numerical data generated by computer program. In this step, ANNs are employed to establish the prediction models of compressive strength and the slump of the concrete. Sensitivity analysis of the ANN is used to evaluate the effect of inputs on the output of the ANN. The two ANN models are tested using data of experimental specimens made in laboratory for twelve different mixtures. The second step is classifying the dataset of pozzolanic concrete mixture proportioning. A classification method is utilized to categorize the dataset into 360 classes based on compressive strength, pozzolanic admixture replacement rate, and material cost. Thus, one can easily obtain mix solutions based on these factors. The results show that the proposed computer-aided approach is convenient for pozzolanic concrete mix design and practical for engineering applications.
    link: http://dx.doi.org/10.1155/2018/4398017
    Appears in Collections:[應用空間資訊系(所)] 期刊論文

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