Abstract
Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.
Highlights
Drilling and blasting is typically used to fragment rock masses at various building and civil construction sites because it is the most economical means of breaking rock for excavation
The test datasets were predicted by EF-2, 3, ISEE model, U.S Bureau of Mines (USBM) model, multivariate linear regression analysis (MLRA), and multivariate non-linear regression analysis (MnLRA) expressed as Equations (14)–(19), respectively, using input parameters of each method
The prediction of peak particle velocity (PPV) using eight predictive analysis methods of artificial neural network (ANN), EF-1, EF-2, EF-3, ISEE model, USBM models, MLRA, and MnLRA with 1191 datasets, which are more than six times the maximum datasets used in the previous studies, was carried out to assess PPV prediction methods at an open-pit construction site
Summary
Drilling and blasting is typically used to fragment rock masses at various building and civil construction sites because it is the most economical means of breaking rock for excavation. The distances between blasting and monitoring points, the charge weights per delay, and the PPVs are monitored and recorded. Based on these factors, K and m are calculated. PPV is predicted using an empirical formula with K, m, the distance between blasting and monitoring points, and the charge weight per delay. This empirical formula often results in significant discrepancies between measured and predicted PPVs. Due to the discrepancies, blasting engineers are forced to use a high factor of safety (FoS) to prevent problems resulting from excessive vibration velocity. A more accurate method of predicting PPV is vital to protect the environment and increase the efficiency of blasting
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