为快速获取隧道施工过程中的地应力，建立基于粒子群优化支持向量回归（PSO-SVR）的随钻-声波隧道地应力估算方法。该方法融合随钻数据与声波信息进行地应力估算： 利用凿岩台车多维随钻数据（冲击压力、推进压力、回转压力、钻速等）以及已知抗压强度、弹性模量和泊松比，建立基于粒子群优化支持向量回归的随钻预测模型，实现对岩石力学参数的高效获取；采集跨孔波速矢量信息，通过波速椭圆拟合获得椭圆特征参数，基于声弹性理论结合预测得到的岩石力学参数推算地应力大小与方向，从而实现地应力的快速估算。为验证本方法的准确性与适用性，在某铁路隧道中开展了现场测试。测试结果表明： 采用该方法测得的地应力大小和方向与传统地应力测试方法的平均误差分别为7.27%和12.51%，单次测试耗时小于2 h，显著提升了地应力测试效率。

To address the technical challenge of rapidly obtaining in-situ stress during tunnel construction, an in-situ stress estimation method for tunnels based on measurement while drilling (MWD) data and acoustic information is developed in this study using particle swarm optimization-support vector regression (PSO-SVR). This method integrates MWD data and acoustic wave information to estimate in-situ stress. By utilizing multidimensional MWD data from a drilling jumbo (including impact pressure, feed pressure, rotation pressure, drilling speed, etc.), together with known compressive strength, elastic modulus, and Poisson′s ratio, a real-time MWD prediction model based on PSO-SVR is established, enabling efficient acquisition of rock mechanical parameters. The cross-hole wave velocity vector information is collated, the elliptical characteristic parameters are obtained through wave velocity ellipse fitting, and the magnitude and direction of in-situ stress are calculated based on the predicted mechanical parameters of rocks by using the acoustic-elastic theory, thus realizing the rapid estimation of in-situ stress. To verify the accuracy and applicability of this method, a field testing program is designed and applied to a plateau railway tunnel. The test results show that the average errors of magnitude and orientation of in-situ stress compared with conventional testing methods are 7.27% and 12.51%, respectively, and the time required for a single test is <2 h, substantially improving the testing efficiency.