# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 7 Conclusions  
In this study, the prediction of PPV using eight predictive analysis methods of 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. Seven key factors relevant to PPV were considered in the prediction models. The seven key factors were selected according to the ease of obtaining them and their influence on PPV. They consist of three factors, VoD, SW, and VoP, newly proposed in this study, and four key factors, W, D, and the site factors K and m, currently included in the conventional empirical formula. The use of three additional influential factors played a significant role in identifying the prediction model that produced the lowest error. Their significant roles were confirmed through a comparison of the performances of the ANN and others. These roles were also apparent in the results of the sensitivity analysis. The seven key factors have similar strengths of relations with PPV. It implies that not only are the previously used factors important in predicting PPV but also the newly added factors. The PPV prediction based on the ANN model achieved the lowest values at MAE, RMSE, and MAPE among the eight prediction models. Even the ANN, which was generalized for application to all sites, produced lower errors than those from the EF-1, which can apply to only a specific site. In addition, the prediction accuracy of the ANN model was higher than that of the ISEE and USBM models. It would be attributed to the ability of ANN to express complex and non-linear relationships between influential factors and PPV, and the ability of ANN to deal with noise. It is necessary to perform a grid search for structures and hyper-parameters and early stopping to obtain an optimal prediction model. In this study, we compared 540 ANN models, to which were applied the early stopping method. These models have one or two hidden layers with the number of nodes calculated using the number of input and output parameters and three activation functions. Finally, a structure consisting of two hidden layers with 21 and 28 nodes using a ReLU as an activation function was determined as the optimal model. Other hyper-parameters were chosen following the previous studies. As a result, we generated the prediction model showing the lowest errors among the six prediction methods. Therefore, we recommend using an ANN for predicting PPV whose hyper-parameters are selected from a grid search and literature research. The EF-2 was proposed by the Ministry of Land, Infrastructure, and Transport in South Korea for designing preliminary blasting patterns. However, the MAE, RMSE, and MAPE associated with the EF-2 were over two times higher than those associated with the EF-3, which is newly proposed in this study. This difference might be a result of different construction types in the datasets. EF-3 was developed by analyzing data from 50 open-pit construction sites, including building construction sites in downtowns, road construction sites, aggregate extraction sites, and restoration work sites while EF-2 was developed by analyzing only datasets at road construction sites. Using the newly proposed EF-3, which proposes a K value of 74.9 and an m value of \(-\)1.535, for a preliminary design of open-pit blasting would be more accurate and reliable than using the EF-2. The ANN model with the seven key factors and EF-3, proposed in this paper, can predict PPVs more accurately and will help blasting pattern design to be more reliable. The reliable blasting patterns will reduce environmental problems significantly and maximize the efficiency of blasting in construction. Moreover, the use of the newly proposed prediction methods will lessen civil complaints, and improve the efficiency in the construction schedule, and reduce the overall construction budgets. These advantages will lead to greater safety and sustainable urban development.