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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/120869


    Title: Using Ultrasonic Pulse and Artificial Intelligence to Investigate the Thermal-Induced Damage Characteristics of Concrete
    Authors: Chen, Li-Hsien;Chen, Wei-Chih;Chen, Yao-Chung;Lin, Hsin-Jung;Cai, Chio-Fang;Lei, Ming-Yuan;Wang, Tien-Chih;Hsu, Kuo-Wei
    Hsu, Kuo-Wei
    徐國偉
    Contributors: 資科系
    Keywords: thermal-induced damage;fire disaster;ultrasonic pulse;wave-velocity ratio;artificial intelligence;WEKA
    Date: 2018-07
    Issue Date: 2018-11-09 15:07:00 (UTC+8)
    Abstract: Using the traditional assessment method considering single-input and single-output variables, the correlation between ignition loss and maximum temperature is usually used to evaluate the fire-damage degree of concrete. To improve this method, multi-input and multi-output variables are examined in this study using a newly-developed experiment consisting of a thermo-induced damage test, ultrasonic pulse (UP) measurement technique, and uniaxial compressive test. The input variables include the designed strength, rate of heating, maximum temperature, and exposure time. The output variables include the stiffness, strength, toughness, and ratio of shear wave velocity to pressure wave velocity (V-s/V-p). Artificial intelligence (AI) is used to assess these variables. The test results show that the stiffness, strength, and toughness decreased with an increase in maximum temperature. The measured V-s/V-p has a high positive correlation with maximum temperature and the reduced ratio of stiffness, strength, and toughness. This correlation was also identified using AI analysis. The findings in this study suggest that the wave velocity ratio obtained using the UP technique can be applied to quantitatively evaluate thermal-induced damage in concrete.
    Relation: APPLIED SCIENCES-BASEL, 8(7), 1107
    Data Type: article
    DOI link: http://dx.doi.org/10.3390/app8071107
    DOI: 10.3390/app8071107
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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