An ANFIS and GEP Models for the Prediction of BIGV in Limestone Quarries

초록

The importance of the blast-induced ground vibration (BIGV) has made researchers to develop various methods such as field measurement, empirical model, multilinear regression, and soft computing have been proposed to quantify its magnitude. Due to the high cost of obtaining the measuring equipment, safety and expertise required in operating the equipment, the blasters depend largely on the empirical models. The most commonly used method is the empirical models and multilinear regression (MLR) method. However, the traditional empirical methods and the MLR model have been found to be unreliable due to large error margin and low correlation coefficient, while the soft computing (SC) method has proven to be more reliable as evident in the literature. Therefore, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) have been proposed in this study to predict the BIGV in limestone quarries. The charge per delay, distance from the measuring station to the blasting point and some controllable blasting parameters were the input parameters, while the PPV was the targeted output. About 23 blasting cases were used to develop the models. The feat of the models was assessed using the statistical indices. The outcomes of the assessments revealed that ANFIS performed better than the GEP model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

키워드