Ground vibration is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, developing a precise model for prediction of ground vibration would be much beneficial to control environmental issues of blasting. The present study proposes a new hybrid machine learning (ML) technique, i.e., autonomous groups particles swarm optimization (AGPSO)–extreme learning machine (ELM) to predict ground vibration resulting from blasting. In fact, AGPSO–ELM model is a modified version of PSO–ELM that can solve problems in a way with higher prediction performance. For comparison purposes, PSO–ELM, minimax probability machine regression, least square–support vector machine and Gaussian process regression models were also proposed to estimate ground vibration. The said ML models were trained and tested based on a database comprising of 102 datasets collected from a quarry site in Malaysia. In the modeling of ML techniques, six input parameters were considered: burden to spacing ratio, maximum charge per delay, stemming, distance from the blasting-face, powder factor and hole depth. The results of ML techniques were evaluated in both stages of training and testing based on five fitness parameters criteria. Considering results of both training and testing datasets, AGPSO–ELM model was able to provide higher prediction performance for PPV prediction. Root-mean-square error values of (0.08 and 0.08) and coefficient of determination values of (0.92 and 0.90) were obtained, respectively, for training and testing datasets of AGPSO–ELM model which revealed that the new hybrid model is capable enough to forecast ground vibration induced by blasting. The newly proposed model can be used in other fields of science and engineering in order to get high accuracy level of prediction.

中文翻译：

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一种预测爆破引起的地面振动的新方法：改进的粒子群优化耦合极限学习机

地面振动是采矿或隧道工程中爆破作业引起的最重要的不良影响之一。因此，开发用于预测地面振动的精确模型将对控制爆破的环境问题大有裨益。本研究提出了一种新的混合机器学习 (ML) 技术，即自主组粒子群优化 (AGPSO)-极限学习机 (ELM) 来预测爆破引起的地面振动。实际上，AGPSO-ELM 模型是 PSO-ELM 的改进版本，可以以更高的预测性能解决问题。为了进行比较，还提出了 PSO-ELM、极小极大概率机器回归、最小二乘支持向量机和高斯过程回归模型来估计地面振动。所述 ML 模型是基于一个数据库进行训练和测试的，该数据库包含从马来西亚采石场收集的 102 个数据集。在 ML 技术的建模中，考虑了六个输入参数：载荷与间距比、每次延迟的最大装药量、堵塞、距爆破面的距离、粉末系数和孔深。ML 技术的结果在训练和测试的两个阶段都基于五个适合度参数标准进行评估。考虑到训练和测试数据集的结果，AGPSO-ELM 模型能够为 PPV 预测提供更高的预测性能。分别获得了（0.08和0.08）的均方根误差值和（0.92和0.90）的决定系数值，用于 AGPSO-ELM 模型的训练和测试数据集，表明新的混合模型足以预测爆破引起的地面振动。新提出的模型可用于其他科学和工程领域，以获得高精度的预测水平。