A NEW METHOD FOR FORECASTING OF BLASTING EFFECT IN ROCK MASS

Gaussian process(GP) is a newly developed machine learning technology based on statistical theoretical fundamentals.It has become a powerful tool for solving highly nonlinear problems.Conventional methods for forecasting of blasting effect in rock mass often meet great difficulty since relationship between blasting effect and its influencing factors is highly complicated nonlinear one.A new method based on GP is proposed for forecasting of blasting effect in rock mass.The method is applied to blasting engineering of the Three Gorges project in China for forecasting of vibration speed,damage depth and damage radius in rock mass.The field experiments of rock blasting are preformed to obtain the training samples and test samples.Nonlinear mapping relationship between blasting effect and its influencing factors can be constructed by GP learning with the training samples.The prediction results for vibration speed,damage depth and damage radius in rock mass using the method are in good agreement with observations.The results of case studies show that the method is feasible,effective and simple to implement for forecasting of blasting effect prediction in rock mass.It has merits of self-adaptive parameters determination and better capacity for solving nonlinear small sample problems comparing with the artificial neural networks method.The good performance of GP model makes it very attractive for a wide range of application in geotechnical engineering.