Using Ultrasonic Pulse and Artificial Intelligence to Investigate the Thermal-Induced Damage Characteristics of Concrete

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 (Vs/Vp). 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 Vs/Vp 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.

Keywords

• thermal-induced damage
• fire disaster
• ultrasonic pulse
• wave-velocity ratio
• artificial intelligence
• WEKA