Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

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 1,191 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.

Assessing Ground Vibration Caused by Rock Blasting in Surface Mines Using Machine-Learning Approaches: A Comparison of CART, SVR and MARS

“…These include hole diameter, hole depth, number of holes, burden, spacing, stemming length, charge per hole, total charge, maximum charge per delay, and monitoring distance. These parameters diversely influence PPV and have been used by various researchers to develop PPV predictive models based on machine-learning technique [9,42,75]. Machine learning is extensively used to solve several prediction problems because of its accuracy as compared to empirical and statistical methods.…”

Metoda zarządzania ryzykiem zawodowym dla robót strzałowych w odkrywkowym górnictwie skalnym w oparciu o zmodyfikowany algorytm FMECA

“…Current research work on blasting safety mainly focuses on the environmental impact of blasting. In the case of blast-induced ground vibrations research was done on prediction of intensity and modeling of ground-borne vibration phenomena [10,11,12,13,14,15], the effect of blast-induced vibrations on buildings and structures [16,17,18], and the influence of blasting technology on the seismic effect [19,20,21]. Considering airblast and flyrock, research was mostly made in prediction models of its intensity [22,23,24,25,26].…”

Prediction of Subsidence during TBM Operation in Mixed-Face Ground Conditions from Realtime Monitoring Data

“…Many attempts to apply ML to geotechnical problems were made even before the advent of DL. Examples include artificial neural networks (ANNs) [14,15,18], adaptive neuro fuzzy-based inference (ANFIS) [12], decision trees (DT) [19], back-propagation neural networks (BPNNs) [15,20,21], support vector minimization (SVM) [17], and gated recurrent units (GRU) [22].…”