Blasting vibration characteristic parameter prediction method based on SA-GA-BP

Abstract

The invention discloses a method for predicting blasting vibration characteristic parameters based on an SAGABP algorithm, and belongs to the technical field of blasting vibration. The method comprises the following steps: collecting blasting vibration influence factors in a blasting engineering field, and then determining hole depth, packing, chassis resistance line, elevation difference, blasting source distance, final assembly charge and maximum section charge as a training sample and a prediction sample according to a main analytic hierarchy process; determining a BP neural network topological structure, calculating an optimal weight value and a threshold value by applying a genetic simulated annealing algorithm (SAGA), and decoding and assigning the optimal weight value and the threshold value to a BP neural network system for training; preliminarily constructing a blasting vibration characteristic parameter prediction model; performing error analysis on a prediction result; and finally, carrying out field prediction on the blasting vibration characteristic parameters in the blasting engineering field. According to the method, the optimal solution can be searched only by optimizing a small number of samples through the improved hybrid intelligent algorithm. Meanwhile, the convergence speed is increased, and the situation of falling into the local optimal solution is avoided.

Description

technical field [0001] The invention relates to a method for predicting characteristic parameters of blasting vibration based on SA-GA-BP, and belongs to the technical field of blasting vibration prediction. Background technique [0002] Blasting technology is being widely used in related earthwork engineering fields such as mining, tunnel excavation, railway and road cutting formation, construction of water conservancy and hydropower infrastructure, and mountain reclamation. The resulting blasting vibration effect has caused huge safety hazards to the surrounding related personnel and facilities, such as the vibration generated during the excavation of the subway tunnel disturbing the residents, and the buildings being destroyed by the blasting seismic wave, etc. Therefore, for blasting engineering, mastering the blasting vibration intensity in advance can avoid many unnecessary accidents. [0003] Generally, blasting vibration intensity is the main criterion composed of p...

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Problems solved by technology

[0003] Generally, blasting vibration intensity is the main criterion composed of particle vibration velocity peak value, vibration frequency, and vibration duration; traditional prediction methods are mainly based on the linear and definite relationship between blasting vibration influencing factors and characteristic parameters, but in fact the relationship has Significant Nonlinearity and Uncertainty

In the prior art, the intelligent optimization algorithm is used to predict some nonlinear parameters, but its prediction effect is often affected by the randomness of the initial weight and threshold of the network system, and it is prone to local oscillation and local optimal solution

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