• Computers and Concrete
• /
• v.12 no.6
• /
• pp.785-802
• /
• 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.

• Computers and Concrete
• /
• v.17 no.6
• /
• pp.787-799
• /
• 2016

Climate anomalies in recent years, numerous natural disasters caused by landslides and a large amount of entrained sands and stones in Taiwan have created significant disasters and greater difficulties in subsequent reconstruction. How to respond to these problems efficaciously is an important issue. In this study, the sands and stones were doped with recycled materials (waste LCD glass sand, slag powder), and material was mixed for recycled ready-mixed soil. The study is based on security and economic principles, using flowability test to determine the water-binder ratio (W/B=2.4, 2.6, and 2.8), a fixed soil: sand ratio of 6:4 and a soil: sand: glass ratio of 6:2:2 as fine aggregate. Slag (at concentrations of 0%, 20%, and 40%) replaced the cement. The following tests were conducted: flowability, initial setting time, unit weight, drop-weight and compressive strength. The results show that the slump values are 220 -290 mm, the slump flow values are 460 -1030 mm, and the tube flow values are 240-590 mm, all conforming to the objectives of the design. The initial setting times are 945-1695 min. The unit weight deviations are 0.1-0.6%. The three groups of mixtures conform to the specification, being below 7.6 cm in the drop-weight test. In the compressive strength test, the water-binder ratios for 2.4 are optimal ($13.78-17.84kgf/cm^2$). The results show that Recycled ready-mixed soil materials (RRMSM) possesses excellent flowability. The other properties, applied to backfill engineering, can effectively save costs and are conducive to environmental protection.