Akademik Veri Yönetim Sistemi
International Graduate Research Symposium, İstanbul, Türkiye, 01 Haziran 2022
The aim of this study is the prediction of blast
vibrations in an Istanbul Region aggregate quarry. Blast-induced vibrations
were measured in the field. Special blast seismographs were used to monitor
ground vibrations. The seismographs measure both particle velocity and
frequency. The measurement location and instantaneous explosive charge were
also measured in each blast. All the blast design parameters were recorded.
Gaussian Process Regression (GPR) method and
classical scaled distance equation were applied to develop prediction
models. Sixty-nine training data and 26
test data were used to construct the forecasting models. The GPR model was
created by the Regression Learner application of Matlab software. In the
modelling stage, GPR model was trained by using 5 different kernel functions:
squared exponential, exponential, rational quadratic, matern 5/2 and matern
3/2. Spacing to burden ratio (S/B), bench height to burden ratio (H/B) and
scaled distance (SD) were chosen as input parameters. The predicted output is
the peak particle velocity (ppv). The scaled distance equation was developed by
univariate regression. Seven error criteria were applied to determine
capability of the developed models. Absolute error metrics, percentage errors
metrics and correlation coefficients were calculated for each model: As a
result, it has been revealed that the GPR model, which is a machine learning
method, is more successful than the classical scaled distance equation. The GPR
model predicts particle velocity with a mean absolute error lower than 1.5 millimeter per second. In addition,
twenty-one cases were forecasted with a mean error lower than 2 mm/s. The GPR
model is not complex and suitable for practical applications.