Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (ρ), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R²) and error analysis. The R² values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model.

爆破岩石具有固有的环境影响，例如地面振动，这可能会干扰生命和财产安全。因此，必须准确预测爆破对环境的影响，因为经验模型并不像文献所证明的那样准确。因此，需要考虑一些鲁棒的预测模型以获得准确的预测结果。提出了基因表达程序（GEP），自适应神经模糊推理系统（ANFIS）和正弦余弦算法优化人工神经网络（SCA-ANN）模型，用于预测五个花岗岩采石场的爆炸引发的地面振动。模型中的输入参数是从爆破点到测量点的距离（D），每次延迟的装料重量（W），岩石密度（ρ）和施密特回弹硬度（SRH）值，而峰值粒子速度（PPV）是目标输出。在开发建议的模型中使用了100个数据集。使用确定系数（R ²）和误差分析来检验所提出模型的性能。从GEP，ANFIS和SCA-ANN模型获得的R ²值分别为0.989、0.997和0.999，而它们的误差接近于零。将提出的模型与经验模型进行比较，发现其性能优于经验模型。