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.
Recalculation of Mass Defects
THE well-known mass defect curve of the old nuclear scheme calculated with regard to α-particles and protons presented a difficulty with its minimum of binding energy for tin and an increasing portion between tin and lead. On the other hand, the mass defect values against protons give a rather smoothly decreasing curve.1 As has already been pointed out,2 the number of α-particles must be considerably reduced from the point of view of the new scheme, which does not admit any electrons in nuclei, but only neutrons and protons (presumably joined as α-particles). The curve of mass defect against protons and neutrons (perhaps with a single ‘central’ α-particle) is very similar to the old curve against protons, but decreases less rapidly. Clearly the new mass defect values relatively to α-particles, neutrons and protons must lie somewhere between the old values computed relative to protons and α-particles respectively (because the number of α-particles is decreased). We may emphasise that this new mass defect curve shows no increasing portion between tin and lead. For illustration we give two typical values: Old: 50Sn124 = 31α + 12&#38;epsiv;; mass defect = 0.158 (in mass units); 82Pb208 = 52α + 22&#38;epsiv;; mass defect = 0.035. New: 50Sn124= 25α + 24ω (ω = neutron); mass defect = 0.304; 82Pb208 = 41α + 44ω; mass defect = 0.366.