An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts

作者：Liao Kuo Wei^*; Fan Jen Chen; Huang Chien Lin

来源：Computers and Geotechnics, 2011, 38(8): 978-986.

DOI：10.1016/j.compgeo.2011.07.008

• Neural network
• Groutability
• Microfine cement
• Permeation grouting

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 (D(10)/D(15)), other important parameters included the void ratio (e), the fines content (FC), the uniformity coefficient (C(u)), the coefficient of gradation (C(z)) 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.

• 出版日期2011-12