An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts |
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Authors: | Kuo-Wei Liao Jen-Chen Fan Chien-Lin Huang |
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Institution: | 1. Department of Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;2. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan |
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Abstract: | The use of microfine cements in permeation grouting has been growing as a strategy in geotechnical engineering because it usually provides improved groutability (N). One of the major challenges of using microfine cement grouts is the ability to estimate the N within a reasonable level of error. The suitability of traditional groutability prediction formulas, which are mostly based on the grain-size of the soil and the grout, is questionable for semi-nanometer scale grout. This study first investigated the accuracy of the current formulas; we found that the accuracy ranges from 45% to 68%, a level that is not adequate for practical engineering. An alternative approach, based on a Radial Basis Function Neural Network (RBFNN), was developed. RBFNN provides a prediction with a 95.8% accuracy within a short time frame. Several parameters were considered in our proposed network; besides the grain-size of the soil (D10/D15), other important parameters included the void ratio (e), the fines content (FC), the uniformity coefficient (Cu), the coefficient of gradation (Cz) and the water-to-cement ratio (w/c). A total of 240 in situ data samples were collected to support the training and testing of the network. After finding a good correlation between the field observation and the RBFNN output, it was concluded that RBFNN is a suitable and reliable tool to predict the outcome of permeation grouting when microfine cement grout is used. |
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Keywords: | Neural network Groutability Microfine cement Permeation grouting |
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