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首页> 外文期刊>Environmental earth sciences >A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model
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A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model

机译：使用基于PSO优化的基于ANFIS的混合模型预测地面振动的新方法

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摘要

Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO-ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO-ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R-2) of 0.984] performed better than the ANFIS with RMSE of 1.61 and R-2 of 0.965. The mentioned results prove the superiority of the newly developed PSO-ANFIS model in estimating blast-produced ground vibrations.
机译：地面振动是采矿业爆破操作的常见环境影响之一，它可能对附近的建筑物和周围居民造成破坏。因此，精确估计爆炸产生的地面振动对于确定爆炸安全区域并最小化环境影响是必要的。在这项研究中，提出了一种通过粒子群优化（PSO）优化的自适应神经模糊推理系统（ANFIS）的混合体，以预测马来西亚Pengerang花岗岩采石场的爆炸产生的地面振动。为此，研究了81次爆破，并精确测量了峰值粒子速度，距爆破面的距离和每个延迟的最大电荷的值。为了演示混合PSO-ANFIS的性能，还开发了ANFIS和美国矿业局的经验模型。预测模型的比较表明，PSO-ANFIS模型[均方根误差（RMSE）为0.48，测定系数（R-2）为0.984]优于ANFIS，RMSE为1.61和R-2为0.965。上述结果证明了新开发的PSO-ANFIS模型在估算爆炸产生的地面振动方面的优越性。

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