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Optimization of smooth blasting parameters for mountain tunnel construction with specified control indices based on a GA and ISVR coupling algorithm

机译:基于GA和ISVR耦合算法的规定控制指标山地隧道光面爆破参数优化。

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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.
机译:平整爆破方法已广泛用于山区隧道的建设中,以减少过大或过大的量,并使隧道轮廓保持设计形状。然而,由于现有优化理论的不足和岩体的复杂性,在具有指定控制指标的任意地质条件下优化光滑爆破参数具有挑战性。在中国长佛岭高速公路隧道的建设过程中,进行了18次现场光面爆破试验。这些实验数据被用作机器学习的训练样本。通过训练这些样本,提出了一种改进的支持向量回归(ISVR)模型,以绘制输入之间的关系图,包括地质条件(岩体的基本质量[BQ]等级,岩石的饱和单轴抗压强度和覆盖层)。深度)和控制指标以及平滑爆破参数的输出,包括周边孔和泄压孔的间距,周边孔的最小负担和线性电荷浓度。遗传算法(GA)与ISVR算法结合使用,可以在训练过程中自动搜索ISVR模型的最佳参数。使用ISVR模型,可以根据围岩的某些地质条件和指定的控制指标(包括树冠沉降,爆破损伤区域(BDZ)的厚度,其中超声波的传播速度为由于爆炸性振动,上冲或下冲的量以及径向去耦比而大大降低。根据Foling隧道的应用结果,ISVR模型被证明是优越的,因为它可以胜过某些现有模型。由于综合考虑了地质条件和控制指标,因此建议的平滑爆破参数的ISVR模型有望更加可行和可靠,因此建议在类似的隧道项目中使用。

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