Mechanical properties of SAP internal curing machinemade sand concrete and BP neural network prediction
1. School of Architectural Engineering, Nantong Institute of Technology, Nantong, Jiangsu 226001, China； 2. Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract：The machinemade sand concrete was mixed with different amounts of super absorbent polymer (SAP) and stone powder, and the compressive strength and flexural strength tests were carried out to investigate the influence of SAP on the mechanical properties of machinemade sand concrete with different amounts of stone powder. Back propagation(BP) neural network was used to predict the compressive strength. The results show that the compressive strength of machinemade sand concrete at various ages is increased first with latter decreasing by increasing SAP content. When SAP content is 008%, the compressive strength is the highest. The addition of 008% SAP can significantly increase the compressive strength with the mixing of various stone powders. The compressive strength of concrete mixed with 9% stone powder has the best effect. The flexural strength of concrete with different stone powder content is decreased first with latter increasing and decreasing by increasing the SAP content. When SAP content is 016 %, the flexural strength reaches the maximum value, and the optimal mixing amount is 016% SAP with 6% stone powder compound. According to the prediction of 48 sets of compressive strength test data, the predicted value is well consistent with the test value.
YANG H F，JIANG J S，LI D K，et al. Analysis on basic mechanical properties and microstructure of concrete made with manufactured sand and recycled coarse aggregate[J].Bulletin of the Chinese Ceramic Society,2018,37(12):3946-3950. (in Chinese)
［3］
MA X W，LIU J H，WU Z M，et al. Effects of SAP on the properties and pore structure of high performance cementbased materials[J]. Construction and Building Materials, 2017,131：476-484.
［4］
DING H Y，ZHANG L，ZHANG P Y. Factors influencing strength of super absorbent polymer (SAP) concrete[J]. Transactions of Tianjin University, 2017, 23(3):245-257.
WEI D B，LI X M，WANG Q C，et al. Study on shrink performance and strength of manufactured sand concrete with super absorbent polymer[J]. Journal of Lanzhou Jiaotong University,2020,39(3):19-24.(in Chinese)
［6］
HAN J G，FANG H，WANG K J. Design and control shrinkage behavior of highstrength selfconsolidating concrete using shrinkagereducing admixture and superabsorbent polymer[J]. Journal of Sustainable CementBased Materials, 2014, 3(3/4): 182-190.
WANG H J，LIU R G，CUI Z W，et al. Mechanical properties of cementbased materials using SAP as internal curing agent modified by nanosilica[J]. Bulletin of the Chinese Ceramic Society, 2020, 39(1):41-49. (in Chinese)
WANG F Z, SHANG D C，QI G H. Effects of SAP on the plastic shrinkage cracking of concrete of high waterbinder ratio[J]. Journal of Building Materials, 2015,18(2):190-194. (in Chinese)
WANG C. Research on GPS height partition fitting method[J]. Applied Mechanics and Materials，2014，580/581/582/583:2860-2864.
[11]
HUANG F C, LIU D X, AN T S, et al. Port container throughput forecast based on ABC optimized BP neural network[J]. IOP Conference Series: Earth and Environmental Science, doi: 10.1088/17551315/571/1/012068.
[12]
GU P, ZHU C M, WU Y Y,et al. Energy consumption prediction model of SiCp/Al composite in grinding based on PSOBP neural network[J]. Solid State Phenomena,2020, 305：163-168.
[13]
赵敏.基于遗传算法优化神经网络的再生保温混凝土强度预测[D].太原：太原理工大学,2018.
［1］
ABDULWAHID M Y. Influences of different stone powders on pervious concrete strength[J]. Structural Concrete,2021,22(1)：528-534.
YANG H F，JIANG J S，LI D K，et al. Analysis on basic mechanical properties and microstructure of concrete made with manufactured sand and recycled coarse aggregate[J].Bulletin of the Chinese Ceramic Society,2018,37(12):3946-3950. (in Chinese)
［3］
MA X W，LIU J H，WU Z M，et al. Effects of SAP on the properties and pore structure of high performance cementbased materials[J]. Construction and Building Materials, 2017,131：476-484.
［4］
DING H Y，ZHANG L，ZHANG P Y. Factors influencing strength of super absorbent polymer (SAP) concrete[J]. Transactions of Tianjin University, 2017, 23(3):245-257.
WEI D B，LI X M，WANG Q C，et al. Study on shrink performance and strength of manufactured sand concrete with super absorbent polymer[J]. Journal of Lanzhou Jiaotong University,2020,39(3):19-24.(in Chinese)
［6］
HAN J G，FANG H，WANG K J. Design and control shrinkage behavior of highstrength selfconsolidating concrete using shrinkagereducing admixture and superabsorbent polymer[J]. Journal of Sustainable CementBased Materials, 2014, 3(3/4): 182-190.
WANG H J，LIU R G，CUI Z W，et al. Mechanical properties of cementbased materials using SAP as internal curing agent modified by nanosilica[J]. Bulletin of the Chinese Ceramic Society, 2020, 39(1):41-49. (in Chinese)
ＷＡＮＧ Ｆ Z， ＳＨＡＮＧ Ｄ C，ＱＩ Ｇ H. Ｅｆｆｅｃｔｓ of ＳＡＰ on the plastic ｓｈｒｉｎｋａｇｅ ｃｒａｃｋｉｎｇ of concrete of high waterbinder ratio[J]. Journal of Building Materials, 2015,18(2):190-194. (in Chinese)
WANG C. Research on GPS height partition fitting method[J]. Applied Mechanics and Materials，2014，580/581/582/583:2860-2864.
[11]
HUANG F C, LIU D X, AN T S, et al. Port container throughput forecast based on ABC optimized BP neural network[J]. IOP Conference Series: Earth and Environmental Science, doi: 10.1088/17551315/571/1/012068.
[12]
GU P, ZHU C M, WU Y Y,et al. Energy consumption prediction model of SiCp/Al composite in grinding based on PSOBP neural network[J]. Solid State Phenomena,2020, 305：163-168.
