Abstract
Rock blasting often has an irreversible impact on the surrounding environment and threatens the safety of life and property. Therefore, accurate prediction of blast-induced ground vibration (BIGV) is a prerequisite for safe construction. In view of the fact that traditional blasting peak particle velocity (PPV) empirical formulas cannot be accurately predicted, this study selected 88 sets of blasting monitoring data, based on distance from the blast-face, maximum charge per delay, total charge, hole depth, spacing, burden, stemming length, and powder factor being used as input variables and PPV being used as output variable to characterize BIGV. First, a nonlinear mapping relationship between input variables and output variable is established through the Gaussian process (GP). The differential evolution algorithm (DE) is used to optimize the hyperparameters σf, σn, and l of the GP, and a blasting PPV model based on the DE-GP is constructed. The proposed model is compared with the empirical formulas, least square support vector machine (LSSVM), artificial neural network (ANN), and GP model, and its prediction performance is evaluated by statistical indicators such as root mean square error (RMSE). Finally, the cosine amplitude method (CAM) is used to analyze the sensitivity of blasting parameters. The results show that the DE-GP algorithm for blasting vibration velocity prediction has higher precision and accuracy, which is significantly better than other models, and is the closest to the measured PPV. Distance from the blast-face, total charge, and maximum charge per delay have a greater impact on the prediction of PPV, while stemming length and powder factor have a smaller impact on the prediction of PPV. The DE-GP model proposed by this research has certain reference value for the prediction and control of PPV in blasting construction.
Highlights
At present, the widespread application of blasting technology has penetrated into many areas of the national economy
According to the previous discussion, we can use the empirical formulas provided in Table 2 to predict the blasting peak particle velocity (PPV) of the monitoring points around the rock foundation pit. ese empirical formulas are mainly determined by the distance from the blast-face and the maximum charge per delay. ey belong to the nonlinear blasting PPV prediction model of rock foundation pits
72 sets of blasting training samples were used for regression analysis to determine the rock properties and terrain conditions in the commonly used blasting PPV prediction formula, as shown in Figure 5. e site constants in the various blasting PPV prediction formulas are shown in Table 4, where PPV is the peak particle velocity at etsreannssomrirtescietitvoesthteheinssigtrnuaml aenndt Sensor
Summary
The widespread application of blasting technology has penetrated into many areas of the national economy. Because of its high efficiency, economy, and speed, it has long been favored in the field of engineering construction [1,2,3]. Blasting produces a series of harmful effects, mainly including blast-induced ground vibration (BIGV), blasting flying rocks, noise, shock wave, etc. BIGV adversely affects surrounding rock masses and nearby structures and even causes damage [6]. The propagation medium of blasting vibration waves is heterogeneous rock mass, and there are many influence factors of PPV [9, 10]. How to accurately predict PPV has become the primary issue in the field of blasting construction safety technology and scientific research [11]
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