Blast Induced Vibration Estimation by Using Different Machine Learning Methods in a Tunnel Excavation

Abstract

This study aimed to develop a new model in which rock characteristics, blasting design parameters and excavation planning were also considered by using various machine learning methods in a funicular line excavation where blast-induced vibrations could not estimate with a high correlation by using the commonly and successfully used PPV-SD (Peak Particle Velocity-Scaled Distance) estimation formula. In addition to developing a new model, another aim was to reveal the effect of rock characteristics, blasting and excavation planning parameters on PPV estimation numerically in the form of weights. For this purpose, 225 events in 57 shots were recorded in the funicular line excavation. Each blasted cross-section's rock characteristics were obtained from on-site inspection and geological reports. At first, recorded blasting vibration data were evaluated using the well-known PPV–SD equation, and it was seen that the relationship between PPV and SD was not able to represent the site-specific vibration attenuation. Therefore, the obtained data were evaluated with Random Forest and other Machine Learning Methods. In these evaluations, RQD, UCS, unit of advance, the maximum charge per delay, the cross-sectional area of tunnel face, total charge, and distance between shot point and vibration measurement station were used as inputs, and peak particle velocity was used as output. The results showed that the random forest model's prediction accuracy was more acceptable than the well-known PPV–SD equation and other machine learning methods. Another significant finding of the study is that parameters not considered in PPV estimation, such as UCS, RQD, and cross-sectional area of tunnel face, may be more effective than the commonly used scaled distance.