基于粒子群优化的PLS-LCF岩爆灾害预测模型研究

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Abstract The prediction of rock burst is a complex systemic problem. It is necessary to consider the various factors affecting the rock burst comprehensively. But when variables to be considered are very large，the multicollinearity among variables will affect the objectivity of the analysis. In order to eliminate the adverse effect and effectively predict rock burst，the affecting factors for rock burst are analyzed by means of partial least-squares regression(PLSR). A new synthesis variable with better interpretation to the dependent variable is extracted and it can preferably overcome the multicollinearity among variables. The nonlinear relationship between composition values and rock burst grades is established according to the logistic curve function(LCF). The particle swarm optimization(PSO) with the global optimization is used to optimize the parameters of LCF. So far，a PLS-LCF prediction model of rock burst based on PSO is built. The test results of the model show a very good precision. The evaluation results obtained by applying the developed model to practical engineering are well consistent with the practical situation，which indicates that the model is feasible and effective for rock burst prediction.

Key words： rock mechanics rock burst prediction multicollinearity partial least-squares regression particle swarm optimization logistic curve function

Received: 13 August 2012

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https://rockmech.whrsm.ac.cn/EN/Y2013/V32/Is2/3180

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