BP Network Based Mix Proportion Design of Self-Compacting Concrete
It was known that many parameters of raw materials, such as, strength of cement, mud content and modulus of fineness of river sand, maximum size of aggregate, content of' needle-like/sheet-like crushed stone, loss of ignition and fineness of fly ash, may exert significant influence on the theology and mechanical properties of self compacting concrete(SCC). It is a dream of researchers to identify the influencing degree of various factors on performance of SCC so as to obtain optimal properties. By virtue of BP neural network approach, this paper employed strength of cement, mud content and fineness modulus of fineness of river sand, maximum size of aggregate, content of needle-like/sheet-like crushed stone, loss of ignition and fineness of fly ash as the input parameters, and the corresponding optimized mix proportion as the output to describe the nonlinear relationship between them. And the orthogonal experiment was designed for the purpose of training and verification of network. The results demonstrated that the pre-trained BP neural network trained by orthogonal test data may employ to predict the optimal concrete mix proportion. This approach may replace some waste-time and heavy laboratory tests. In addition, such method may real-time optimize mixture proportion. of self-compacting concrete, which has great effect on the quality control of manufacturing self-compacting concrete.
A functional network to predict fresh and hardened properties of self‐compacting concretes
Literature offers several examples of application of neural networks to predict fresh and hardened properties of conventional and self‐compacting concretes. In this paper, these issues are addressed to a relatively recent computational scheme given by the functional networks, by proposing an initial simple application. The algorithm with some appropriate assumptions, reveals a quick convergence and an interesting generalization capability even if the training data set is poor. Copyright © 2009 John Wiley & Sons, Ltd.
Experimental study on bond behavior between high-strength grout and deformed steel bars
Abstract Engineers use high-strength grout to connect precast concrete members for its low shrinkage and good fluidity. This paper designed 87 central pull-out specimens using the control variable method to investigate the influence of factors on the rebar-grout bond property such as compressive strength, steel grade, bar diameter, transverse stirrups, and bond length. The test result showed that improving grout compressive strength (85.8 MPa–122.7 MPa) cannot improve the bond strength, which was different from the previous conclusions of concrete pull-out tests. Simplified formulas were presented for calculating the characteristic values of bond strength, residual strength, peak slip, and relative slip of load-holding platform based on parameter analysis. Then, a constitutive model was proposed to predict the full-range bond stress-slip curve of high-strength grout and steel bar specimens from bearing to failure. Finally, FEA models were established to evaluate the applicability and accuracy of the constitutive model.