# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 1 Introduction  
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. However, blasting at construction sites is accompanied by undesirable environmental side effects, such as vibration, noise, and scattering of debris. According to Korea's Office of National Environmental Conflict Resolution Commission, 3840 (approximately 84%) of the 4557 environmental dispute cases on record involve noise and vibration, primarily from construction sites [1]. blasting vibrations occurring at a construction site account for the majority of these environmental disputes because they result in damage to nearby structures and present various safety concerns. Every country specifies a limit on the peak particle velocity (PPV) of the induced vibrations to minimize damage to nearby structures. According to DIN 4150-3 [2], the limits on PPV are 2 cm/s for buildings used for commercial purposes, 0.5 cm/s for dwellings, and 0.3 cm/s for buildings under preservation orders at a frequency of 1 to 10 Hz. [PERSON] et al. [3] proposed that 1.9 and 1.3 cm/s are safe levels of blasting vibration for drywall and plaster under 10 Hz conditions. In South Korea, the limits on PPV are 0.2 cm/s for cultural assets and 0.5 cm/s for apartments. blasting engineers try to accurately predict PPVs that will be induced by blasting and apply the predicted PPVs to the design of blasting patterns to comply with these regulations. Many researchers have studied and proposed various empirical formulas to predict and control PPV [4]. Among the various empirical formulas, a conventional empirical formuladeveloped by U.S. Bureau of Mines (USBM) researchers, [PERSON] [5] has been widely used to predict PPV and design blasting patterns. The current design approach consists of two steps. First, several test blastings are conducted to determine site factors K and m, which represent geological characteristics, before massive blasting. At each test, 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. Second, PPV is predicted using an empirical formula with K, m, the distance between blasting and monitoring points, and the charge weight per delay. However, 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 high FoS typically requires the use of a more conservative charge weight per delay than the maximum allowable weight would accommodate. The conservative charge weight per delay can decrease blasting efficiency and increase construction time and total cost. A more accurate method of predicting PPV is vital to protect the environment and increase the efficiency of blasting. The artificial neural network (ANN) has been applied in various fields such as renewable energy systems [6], atmospheric science [7], and civil engineering [8; 9] to predict targets. In addition, research is also ongoing on predicting PPVs using ANN. To develop an ANN model for PPV prediction, [PERSON] et al. [10] gathered 185 blasting datasets from a limestone mine in Vietnam, [PERSON] et al. [11] collected 70 blasting datasets from a copper mine in Iran, and [PERSON] et al. [12] obtained 83 blasting datasets from a quarry mine in Vietnam. Every result of the research showed good agreement with the measured and predicted PPVs. ANN is generally not limited by any assumptions such as linearity or normality, thus ANN has the modeling power to derive excellent results even with irregular datasets and complex phenomena [13; 14]. However, in the previous studies, the largest number of datasets was only 185 and the datasets were obtained from a limited local region.