Abstract

The smooth blasting method has been widely used in the construction of mountain tunnels to decrease the volume of overbreak or underbreak and maintain the tunnel outline in the design shape. However, due to the shortcomings of existing optimization theories and the complexity of rock masses, optimizing the smooth blasting parameters in arbitrary geological conditions with specified control indices is challenging. Eighteen on-site smooth blasting experiments were conducted during the construction of the long Foling highway tunnel in China. These experimental data were used as the training samples for machine learning. By training these samples, an improved support vector regression (ISVR) model was proposed to map the relation between the inputs, comprising the geological conditions (the basic quality [BQ] grade of the rock mass, saturated uniaxial compression strength of rock, and overburden depth) and control indices and the outputs of the smooth blasting parameters, including the spacing of perimeter holes and relief holes, minimum burden and linear charge concentration of perimeter holes. A genetic algorithm (GA) was coupled with an ISVR algorithm to automatically search the optimal parameters of the ISVR model during the training process. Using the ISVR model, the optimization of smooth blasting parameters can be obtained based on certain geological conditions of surrounding rock and specified control indices, including the crown settlement, thickness of the blasting damage zone (BDZ) in which the travelling velocity of ultrasonic waves is reduced significantly due to explosive vibration, volume of overbreak or underbreak, and radial decoupling ratio. According to the application results of the Foling tunnel, the ISVR model was shown to be superior since it can outperform certain existing models. As geological conditions and control indices are comprehensively considered, the proposed ISVR model of smooth blasting parameters is expected to be more feasible and reliable and is thus recommended for use in similar tunnel projects.

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