Aimed at the complicated nonlinear relation between the factors influencing the blasting vibration velocity, a blasting vibration velocity prediction model is built by using the particle swarm optimization (PSO) global search optimal solution principle and extreme learning machine (ELM) ability which can deal with the nonlinear relationship. Taking blasting vibration measured data in a certain area as an example, the total dose, the explosive charge, the distance between shot and monitoring point, the ground vibration velocity and the height of the monitoring point are selected as input variables and the building vibration velocity is chosen as the output variable. The result shows that the mean square errors between training value and predicted value and between test value and predicted value are 0.18 and 2.56, respectively, and the average relative error is controlled within 6%. It is proved that the model has good precision and generalization ability. Compared with the traditional ELM model, the PSO-ELM model not only improves the accuracy and generalization ability, but also reduces the influence on the result of training when the numbers of training samples and the hidden layer nodes change, thus the fitting ability of the model is improved. This model has great a promotional value in similar predictive engineering.
WANG Xinmin
,
WAN Xiaoheng
,
ZHU Yangya
,
JIANG Zhiliang
,
LIU Jixiang
. Prediction for Building Vibration Velocity Caused by Blasting Based on PSO-ELM[J]. Science & Technology Review, 2014
, 32(19)
: 15
-20
.
DOI: 10.3981/j.issn.1000-7857.2014.19.001
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