A back propagation (BP) neural network (NN) model was used to analyze the relationship between the cube compressive strength and various strength indicators of concrete with large-sized recycled aggregates (LSRA) (80 mm maximum size). Factors such as strength and replacement rate of recycled aggregates were used as input parameters to establish the neural network model. The BP-NN model was optimized by analyzing the influence and sensitivity of each parameter in the model. Then the mechanical properties of concrete with LSRA were predicted. Results showed that the strength of new concrete had a more significant impact on the strength of recycled concrete with LSRA, followed by the strength of old concrete. While considering all the factors, including the mechanical strength and the replacement ratio regarding the maximum utilization of RA, the 30% incorporation rate was suggested as an ideal incorporation rate.
CaiBXuLFFuF (2019) Shear resistance prediction of post-fire reinforced concrete beams using artificial neural network. International Journal of Concrete Structures and Materials13(1): 46.
2.
DeshpandeNLondheSKulkarniS (2014) Modeling compressive strength of recycled aggregate concrete by artificial neural network, model tree and non-linear regression. International Journal of Sustainable Built Environment3(2): 187–198.
3.
DuanZHouSPoonC-S, et al. (2018) Using neural networks to determine the significance of aggregate characteristics affecting the mechanical properties of recycled aggregate concrete. Applied Sciences8(11): 2171.
4.
GuoZ (2011) Experimental Study on Mechanical Properties of Concrete Contained Waste Concrete: [D]. Shanghai: University of Tongji.
5.
HammoudiAMoussacebKBelebchoucheC, et al. (2019) Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Construction and Building Materials209: 425–436.
6.
HanIJYuanTFLeeJY, et al. (2019) Learned prediction of compressive strength of GGBFS concrete using hybrid artificial neural network models. Materials (Basel, Switzerland)12(22): 3708.
7.
KhademiFJamalSMDeshpandeN, et al. (2016) Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment5(2): 355–369.
8.
KimSChoiHBShinY, et al. (2013) Optimizing the mixing proportion with neural networks based on genetic algorithms for recycled aggregate concrete. Advances in Materials Science and Engineering2013: 680–685.
9.
KoneruVSGhorpadeVGGhorpade (2020) Assessment of strength characteristics for experimental based workable self compacting concrete using artificial neural network. Materials Today: Proceedings26(Pt 2): 238–1244.
10.
LiTXiaoJZhuC, et al. (2016) Experimental study on mechanical behaviors of concrete with large-size recycled coarse aggregate. Construction and Building Materials120: 321–328.
11.
MadaniHKooshafarMEmadiM (2020) Compressive strength prediction of nanosilica-incorporated cement mixtures using adaptive neuro-fuzzy inference system and artificial neural network models. Practice Periodical on Structural Design and Construction25(3): 04020021.
12.
NaderpourHRafieanAHFakharianP, et al. (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering16: 213–219.
13.
OhBKParkYParkHS (2020) Seismic response prediction method for building structures using convolutional neural network. Structural Control and Health Monitoring27(5): e2519.
14.
TanK (2018) Predicting compressive strength of recycled concrete for construction 3D printing based on statistical analysis of various neural networks. Journal of Building Construction and Planning Research6(2): 71–89.
15.
TranVLThaiDKNguyenDD (2020) Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete. Thin Wall Structures151: 106720.
XuJChenYXieT, et al. (2019a) Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques. Construction and Building Materials226: 534–554.
18.
XuJZhaoXYuY, et al. (2019b) Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks. Construction and Building Materials211: 479–491.
19.
YongWX (2020) Analysis of the effect of fly ash on compressive strength of concrete using neural network and parameter analysis. Materials Science Forum5960: 130–135.
20.
ZhouYZhengSHuangZ, et al. (2020) Explicit neural network model for predicting FRP-concrete interfacial bond strength based on a large database. Composite Structures240: 1879–1085.
