A Study on the Groutability of Permeation Groutingith Microfine Cement Grout to Sandy Silt Soils
Date Issued
2009
Date
2009
Author(s)
Huang, Sheng-Hsiu
Abstract
In this study, 240 sets of field data were collected and analyzed to evaluate the groutability by using two methods, namely the conventional formula with relative particle size ratio and the backpropagation neural network(BPN). The accuracy of the conventional formula method ranged from 45% to 68%, i.e., this method can not be successfully used to predict the groutability. Seven factors affecting the groutability were used in the BPN methods；they are： the effective soil particle size (d10), the soil particle size(d15), void ratio(e), fines content(FC), uniformity coefficient(Cu), coefficient of gradation(Cz) and the water-to-cement ratio(W/C). These factors used as the neuron of the neural network input layer to establish a suitable network model which may be used to predict the groutability of permeation grouting with microfine cement grout to the sandy silt soils with high content of fines in Taiwan. From the obtained results, it can be found that while the soil particle size(d15), void ratio(e), fines content(FC), uniformity coefficient(Cu), coefficient of gradation(Cz) and the water-to-cement ratio(W/C) were used as the neuron of the input layer, the BPN method showed a better forecast ability with an accuracy as high as 96%. Aside from these, in this study, the permeation grouting experiments were also conducted in the laboratory. The water-to-cement ratio were controlled to be 3.34, 4.0 and 4.65, which were the same as the value used in the field. The slag content of the microfine cement is 50% and five different contents of fines, namely, 0%, 10%, 20%, 30% and 40%, were used. Using the data obtained from the permeation grouting experiments, the BPN forecasting model were verified and its accuracy reached 87%. According to the results of this study, the conventional formula method could not be successfully used to predict the groutability of the permeation grouting with microfine cement grout to sandy silt soils. However, while dealing with these problems, the BPN model showed its superiority and practicality.
Subjects
backpropagation neural network (BPN)
groutability
microfine cement
permeation grouting
Type
thesis
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