Prediction of Grouting Method for Decreasing the Lugeon Value of Dam Foundation

Bambang Risharnanda, Suhardjono, H. Andre Primantyo, Runi Asmaranto


This research aims to predict the grouting method to decrease the Lugeon value on the dam foundation. The methodology consisting of grouting is evaluated by comparing the first permeability (permeability before grouting) and secondary permeability (permeability after grouting). Grouting is critical in improving the dam foundation and reducing the permeability in Lugeon values. Because of the concealment and uncertainty of dam foundation grouting, the accurate evaluation of the quality of grouting construction has become a major concern. The effectiveness of the grouting method for decreasing Lugeon values in the dam foundation has not been certainly revealed. The foundation of the dam with weathered lapilli tuff and gravelly sand is a typical porous weathered rock. The grouting was performed as a down-stage, in which the grout hole is drilled in stages using a pneumatic packer. The improvement of the dam foundation by grouting is evaluated by comparing the first permeability (permeability before grouting) and secondary permeability (permeability after grouting) by using Lugeon values with a water pressure test. This test employed 534 data, which included the first permeability, depth, grout take, and secondary permeability. This paper focuses on modeling to predict the secondary permeability. The result is developing the secondary permeability that considers the relation between the parameters, which can affect and represent the characteristics of grouting quality in the non-linear regression equation that is SP = FP – f(GT) in the depth conditions of 5, 10, 15, 20, 25, and 30 m. R-squared is the coefficient of determination. The R-squared value for depths of 5, 10, 15, 20, 25, and 30 m is 87.52%, 83.18%, 72.29%, 60.20%, 42.25%, and 30.03%. Thus, the results are applicable to assess the quality of curtain grouting.


Keywords: dam foundation, grouting, permeability, quality assessment, the nonlinear regression methods.

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