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BULLETIN OF THE CHINESE CERAMIC SOCIETY ›› 2021, Vol. 40 ›› Issue (2): 455-464.

Special Issue: 水泥混凝土

• Cement and Concrete • Previous Articles     Next Articles

Strength Prediction of Mechanical Properties of Polypropylene Fiber Reinforced Concrete after High Temperature Based on Machine Learning

LIANG Ninghui1,2, YOU Xiufei1,2, CAO Guojun1,2, LIU Xinrong1,2, ZHONG Zuliang1,2   

  1. 1. College of Civil Engineering, Chongqing University, Chongqing 400045, China;
    2. National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing University, Chongqing 400045, China
  • Received:2020-10-13 Revised:2020-11-22 Online:2021-02-15 Published:2021-03-10

Abstract: There are many factors affecting the mechanical properties of polypropylene fiber reinforced concrete (PFRC) after high temperature. Therefore, the relevant experimental period is long and experimental volume is large. How to use the existing experimental data to predict the strength of PFRC after high temperature effectively improves the test efficiency and provide reference for practical projects. By studying the influences of fiber scale, fiber content and temperature on concrete strength, three models, namely regression tree (RT), support vector regression (SVR) and BP artificial neural network, were established. The experimental values of splitting tensile strength and compressive strength of PFRC at different heating temperatures (20 ℃, 200 ℃, 400 ℃, 600 ℃, 800 ℃) were compared with the predicted values. The results show that three models predict the splitting tensile strength and compressive strength of PFRC at high temperature with high precision. Compared with the measured value, the relative error between the predicted value and the measured value of three models are basically controlled within 15%, except for individual data. By comparing the mean absolute error (UMAE) and average correlation coefficient R2 of three models, the prediction results of artificial neural network (ANN) model are better, which verifies the reliability of machine learning in predicting the mechanical properties of PFRC after high temperature.

Key words: machine learning, polypropylene fiber, concrete, high temperature, strength prediction

CLC Number: