Experimental Study On Crumb Rubber Lightweight Concrete

The paper presents the experimental study which aims at characterizing the basic mechanics property of the crumb rubber lightweight concrete(CRLC). The results show that the CRLC has good capability of distortion and arrest cracks. The disfeature of CRLC is different of normal lightweight concrete's brittleness. It shows that the crumb rubber aggregate can improve the lightweight concrete's tractility.The stress-strain responses of CL30 with 4 levels of rubber volumes are conducted under uniaxial compression. It is shown that those responses can be graphically interpolated to well follow the model derived by Guo Zhenhai's but with different fitting coefficients. The peak stress, convergence stress and inflection point stress vary linearly with the mixture of crumb aggregate. The water permeability tests are also preformed and the results show adding rubber crumbs help reduce water permeability.The paper presents an experimental and analytical study on the issue of aggregate separation for crumb rubber lightweight concrete (CRLC). The systematic measures are carried out to quantify the spatial distribution of lightweight aggregates, and then the particle dynamic equation and the orthogonal design method are employed, which contains four factors: w/c ratio within the range of designed compressive strength, sand rate, crumb rubber volume and water reducers, to analyze those measurements. The results show that adding crumb rubber will reduce the aggregate velocity and effectively improve the uniformity of spatial distribution of lightweight aggregates. The sequences of the significance for the four factors on the uniformity of distribution of lightweight aggregates are: crumb rubber volume, water reducers, sand rate and w/c ratio.Based on neural network , a three layers BP neural network model was given to forecast the uniformity of crumb rubber lightweight concrete (CRLC). The model was trained according to the test date. The results show that curves obtained by BP neural network model have preferable accuracy. So it could be used to predict the uniformity of crumb rubber lightweight concrete.