为了在TBM不停机的条件下根据岩渣特征提前判断掌子面围岩条件，建立TBM岩渣图像采集系统，对各类围岩条件下产生的渣片图像进行采集，借助二值化与分水岭分割方法对渣片图像进行处理，选取曲率系数、不均匀系数、最大粒径和粗糙度指数作为岩渣的级配特征指标，分别采用线性、对数、逆、二次、幂、复合、S曲线和增长共8种模型建立岩渣级配参数与围岩类别、岩石单轴抗压强度、岩石完整性系数之间的关系。结果表明： 1）随着围岩条件变差，岩渣的曲率系数逐渐减小，不均匀系数、最大粒径、粗糙度指数逐渐增大； 2)随着岩石单轴抗压强度增大，岩渣的曲率系数呈增大趋势，不均匀系数、最大粒径、粗糙度指数呈减小趋势； 3)随岩石完整性系数增加，岩渣曲率系数呈增大趋势，不均匀系数、最大粒径、粗糙度指数呈减小趋势。
A slagimage acquisition system of tunnel
boring machines(TBMs) is developed herein to predict the surrounding rock
conditions at the excavation face in advance based on the characteristics of
rock slag without halting the TBM. Slag images are captured under various
surrounding rock conditions and processed using binarization and watershed
segmentation methods. The curvature coefficient, nonuniformity coefficient,
maximum particle size, and roughness index are used as slag grading
characteristic indices. Eight models, namely linear, logarithmic, inverse,
quadratic, power, compound, Scurve, and growth, are
employed to establish relationships among rockslag
grading parameters and the types, uniaxial compressive strengths, and integrity
coefficients of surrounding rocks. The results indicate the following: (1) The
curvature coefficient of slag decreases while the nonuniformity coefficient,
maximum particle size, and roughness index increase as surrounding rock
conditions deteriorate. (2) The curvature coefficient of rock slag increases
while the nonuniformity coefficient, maximum particle size, and roughness index
decrease with increasing uniaxial compressive strength of rock. (3) The
curvature coefficient of rock slag increases while the nonuniformity
coefficient, maximum particle size, and roughness index decrease with
increasing rock integrity coefficient.
