• Computers and Concrete
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• 제17권3호
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• pp.423-433
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• 2016

After the industrial of steelmaking by-products are processed properly, they can be used in civil engineering, not only as a substitute for natural resources and to reduce costs, but also to provide environmental protection. This study used different amounts (10%, 20%, 30%, 40%,and 50%) of desulphurization slag to replace natural fine aggregates in ready-mixed soil materials, and tested the physical and fresh properties (slump, slump flow, tube flow, initial setting time, and bleeding) and hardened properties (compressive strength, ball drop, ultrasonic pulse velocity) of the materials. The variations between the performances of the materials with different mix proportions were discussed. When desulphurization slag is used in RMSM, the workability can be enhanced obviously significantly. When the replacement of desulphurization slag is 50%, the slump flow is increased by 110mm compared with the control group, and the initial setting time increases as the replacement increases, because of bleeding. When the replacement is 10% and 20%, the compressive strength at various ages is higher than that of the control group. When the replacement is 10%, the compressive strength at 7 days is higher than that of the control group by 60%, and the ultrasonic pulse velocity is proportional to the compressive strength, which increases with age and decrease as the replacement increases. An appropriate replacement can effectively accelerate construction, and allow projects to be finished ahead of schedule; therefore, an appropriate replacement, is applicable for ready-mixed soil materials.

• Computers and Concrete
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• 제12권6호
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.