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.
Highlights
A reinforced concrete (RC) structure is the main system for buildings and construction in Taiwan
To identify these experimental results, the data mining/machine learning method was used to evaluate the correlation between thermal conditions, mechanical parameters, and the ultrasonic pulse (UP) wave velocity ratio
This study was the first to apply ultrasonic pulse measurement to investigate the thermal-induced damage characteristics of concrete by conducting the uniaxial compressive test after concrete was subjected to heating under different thermal conditions
Summary
A reinforced concrete (RC) structure is the main system for buildings and construction in Taiwan. Tovey [1] proposed several methods such as the color change identification method, core experiment, ultrasonic pulse (UP) measurement, and the correlation of concrete strength reduction with temperature to evaluate the degree of fire-damage. A new thermo-solid damage experiment that consisted of the thermo-induced damage test, UP measurement technique, and uniaxial compressive test was established. To identify these experimental results, the data mining/machine learning method was used to evaluate the correlation between thermal conditions (the rate of heating, maximum temperature, exposure time, as well as cooling condition), mechanical parameters (stiffness, strength, and toughness), and the UP wave velocity ratio
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