Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure

Abstract

Air overpressure (AOp) induced by rock blasting is an undesirable phenomenon in open-pit mines and civil construction works. The prediction of AOp has been always a complicated task since many parameters have potential to affect the propagation of air waves. This study aims to assess the capability of a new hybrid evolutionary model based on an integrated adaptive neuro-fuzzy inference system (ANFIS) with a stochastic fractal search (SFS) algorithm. To assess the reliability and acceptability of ANFIS-SFS model, the particle swarm optimization (PSO) and genetic algorithm (GA) were also combined with ANFIS. The proposed models were developed using a comprehensive database including 62 sets of data collected from four granite quarry sites in Malaysia. Performances of the ANFIS-SFS, ANFIS-GA, and ANFIS-PSO models were checked using statistical functions as the performance criteria. The obtained results showed that the proposed ANFIS-SFS model, with root mean square error of 1.223 dB, provided much higher generalization capacity than the ANFIS-PSO (RMSE of 1.939 dB), ANFIS-GA (RMSE of 2.418 dB), and ANFIS (RMSE of 3.403 dB) models in terms of predicting AOp. This clearly demonstrates the effectiveness of SFS to provide a more accurate model in the AOp prediction field.