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.
STUDY ON THE APPLICATION OF BACK PROPAGATION NEURAL NETWORK TO CONFECT HIGH STRENGTH CONCRETE
Based on the theory of neural network. the technical experiences and route of the compounding and construction in high strength pumping concrete, the author suggests a BP neural network approach to solve the problems of modular ambiguity, the multiple random factors and the nonlinear influence of the composition to the strength of the concrete. Accordingly, he develops an applicable software for mix proportion of concrete which is capable of forecasting the strength and the workability of the high strength concrete. \[
Evaluation of buckling load and dynamic performance of steel shear wall retrofitted with strips made of shape memory alloy
One of the most imperfections of the steel shear wall is out of plane displacements that cause severe damage in both structural and non-structural elements. In this paper, the effects of shape-memory alloys on steel shear walls are investigated. First, a numerical analysis using the finite element method in ABAQUS software has been carried out according to an experimental test. The results of the numerical analysis have been verified with experimental results. Next, shape-memory alloy fibers have been added vertically and horizontally to various parts of the steel shear wall. The results show that retrofitting with the shape-memory alloy reduces the out-of-plane displacement of the steel shear wall under both cyclic and seismic loadings. Besides, the buckling load in the steel shear wall increases when it is retrofitted with the shape-memory alloy. Also, the total out-of-plane movement (accumulated absolute displacements) of the steel shear wall and non-structural damage are controlled by the characteristic of a shape-memory alloy material called “super-elastic”.