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

Abstract

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 (U_MAE) 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:

TU528.572

LIANG Ninghui, YOU Xiufei, CAO Guojun, LIU Xinrong, ZHONG Zuliang. Strength Prediction of Mechanical Properties of Polypropylene Fiber Reinforced Concrete after High Temperature Based on Machine Learning[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2021, 40(2): 455-464.

References

[1] LI M, QIAN C X, SUN W. Mechanical properties of high-strength concrete after fire[J]. Cement and Concrete Research, 2004, 34(6): 1001-1005.
[2] 余志武,丁发兴,罗建平.高温后不同类型混凝土力学性能试验研究[J].安全与环境学报,2005,5(5):1-6.
YU Z W, DING F X, LUO J P. Experimental research on mechanical properties of different type of concrete after high temperature[J]. Journal of Safety and Environment, 2005, 5(5): 1-6 (in Chinese).
[3] 司秀勇,潘慧敏.纤维对混凝土早期抗裂性能的影响[J].硅酸盐通报,2011,30(6):1425-1429.
SI X Y, PAN H M. Influences of fiber on early crack resistance of concrete[J]. Bulletin of the Chinese Ceramic Society, 2011, 30(6): 1425-1429 (in Chinese).
[4] 金凤杰,许金余,范飞林,等.钢纤维混凝土的高温动态强度特性[J].硅酸盐通报,2013,32(4):683-686.
JIN F J, XU J Y, FAN F L, et al. Strength property of steel fiber reinforced concrete at elevated temperature[J]. Bulletin of the Chinese Ceramic Society, 2013, 32(4): 683-686 (in Chinese).
[5] 张琦,高雪,杜红秀.聚丙烯纤维对高强混凝土高温作用后氯离子渗透性的影响研究[J].混凝土,2015(3):87-89.
ZHANG Q, GAO X, DU H X. Study on the effect of polypropylene fiber about chloride permeability of high performance concrete after high temperature[J]. Concrete, 2015(3): 87-89 (in Chinese).
[6] 吴佳,杜红秀,郝晓玉.聚丙烯纤维长径比对高温后高性能混凝土抗压强度的影响[J].混凝土,2016(3):65-67+75.
WU J, DU H X, HAO X Y. Effect of the aspect ratio of polypropylene fiber on high strength concrete after exposure to high temperature[J]. Concrete, 2016(3): 65-67+75 (in Chinese).
[7] 柳献,袁勇,叶光,等.高性能混凝土高温爆裂的机理探讨[J].土木工程学报,2008,41(6):61-68.
LIU X, YUAN Y, YE G, et al. Investigation on the mechanism of explosive spalling of high performance concrete at elevated temperatures[J]. China Civil Engineering Journal, 2008, 41(6): 61-68 (in Chinese).
[8] 牛旭婧,赵庆新,陈天红.聚丙烯粗纤维对高强混凝土高温后性能影响[J].硅酸盐通报,2013,32(12):2583-2588.
NIU X J, ZHAO Q X, CHEN T H. Effect of polypropylene macro-fiber on properties of high-strength concrete after being exposed to high temperature[J]. Bulletin of the Chinese Ceramic Society, 2013, 32(12): 2583-2588 (in Chinese).
[9] 李地红,高群,夏娴,等.基于BP神经网络的混凝土综合性能预测[J].材料导报,2019,33(s2):317-320.
LI D H, GAO Q, XIA X, et al. Prediction of comprehensive performance of concrete based on BP neural network[J]. Materials Reports, 2019, 33(s2): 317-320 (in Chinese).
[10] HADZIMA-NYARKO M, NYARKO E K, LU H F, et al. Machine learning approaches for estimation of compressive strength of concrete[J]. The European Physical Journal Plus, 2020, 135(8): 682.
[11] CHOPRA P, SHARMA R K, KUMAR M, et al. Comparison of machine learning techniques for the prediction of compressive strength of concrete[J]. Advances in Civil Engineering, 2018, 2018: 1-9.
[12] TANYILDIZI H. Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine[J]. Advances in Civil Engineering, 2018, 2018: 1-10.
[13] CHEN B T, CHANG T P, SHIH J Y, et al. Estimation of exposed temperature for fire-damaged concrete using support vector machine[J]. Computational Materials Science, 2009, 44(3): 913-920.
[14] YANG H Y, DONG Y F. Modelling concrete strength using support vector machines[J]. Applied Mechanics and Materials, 2013, 438/439: 170-173.
[15] 梁宁慧,曹郭俊,刘新荣,等.基于三点弯曲试验的聚丙烯纤维桥接应力研究[J].材料导报,2020,34(2):2153-2158.
LIANG N H, CAO G J, LIU X R, et al. Study on bridging stress of polypropylene fiber based on three-point bending test[J]. Materials Reports, 2020, 34(2): 2153-2158 (in Chinese).
[16] 梁宁慧,刘新荣,孙霁.多尺度聚丙烯纤维混凝土单轴拉伸试验[J].重庆大学学报,2012,35(6):80-84+124.
LIANG N H, LIU X R, SUN J. Uniaxial tensile test of multi-scale polypropylene fiber reinforced concrete[J]. Journal of Chongqing University, 2012, 35(6): 80-84+124 (in Chinese).
[17] 张海燕,刘岩,马丽萌,等.决策树算法的比较与应用研究[J].华北电力技术,2017(6):42-47.
ZHANG H Y, LIU Y, MA L M, et al. Comparison and application of decision tree algorithm[J]. North China Electric Power, 2017(6): 42-47 (in Chinese).
[18] 段小手.深入浅出Python机器学习[M].北京:清华大学出版社,2018.
DUAN X S. Start with Python machine learning[M]. Beijing: Tsinghua University Press, 2018 (in Chinese).
[19] 胡鑫.基于人工神经网络的HPC强度预测[D].长沙:湖南大学, 2014.
HU X. Prediction of high performance concrete strength based on artificial neural network[D]. Changsha: Hunan University, 2014 (in Chinese).
[20] 周志华.机器学习[M].北京:清华大学出版社,2016.
ZHOU Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016 (in Chinese).
[21] YAN K Z, SHI C J. Prediction of elastic modulus of normal and high strength concrete by support vector machine[J]. Construction and Building Materials, 2010, 24(8): 1479-1485.
[22] 王继宗,倪宏光.基于BP神经网络的水泥抗压强度预测研究[J].硅酸盐学报,1999,27(4):3-5.
WANG J Z, NI H G. Prediction of compressive strength of cement based on BP neural networks[J]. Journal of the Chinese Ceramic Society, 1999, 27(4): 3-5 (in Chinese).
[23] 沈花玉,王兆霞,高成耀,等.BP神经网络隐含层单元数的确定[J].天津理工大学学报,2008,24(5):13-15.
SHEN H Y, WANG Z X, GAO C Y, et al. Determining the number of BP neural network hidden layer units[J]. Journal of Tianjin University of Technology, 2008, 24(5): 13-15 (in Chinese).