In this paper, blast-induced ground vibration (BIGV) was considered as the primary objective, and a new artificial intelligence system was proposed to predict BIGV with high accuracy based on the M5Rules and imperialist competitive algorithm (ICA), called ICA–M5Rules technique. Accordingly, the ICA was considered to optimize the M5Rules based on the rules of the M5 model, as well as the prune and smooth procedures. To evaluate the effectiveness of the proposed ICA–M5Rules technique, random forest (RF), classical M5Rules, and support vector machine (SVM) were developed as the benchmark techniques to compare with the proposed ICA–M5Rules technique. Besides, two existing empirical equations were each used to develop models based on the experimental datasets to estimate BIGV for comparison with the proposed ICA–M5Rules model. A case study of a quarry mine in Vietnam was adopted for the developed (ICA–M5Rules, M5Rules, RF, SVM, empirical) models based on 125 blasting events. Mean absolute error, root-mean-squared error, and determination of coefficient (R²) were applied and computed to evaluate the accuracy, as well as the performance of the developed models. The findings indicated that the proposed ICA–M5Rules model provided highest accuracy. The primary objective was appropriately addressed based on the obtained results of the developed models. As well established, the proposed ICA–M5Rules model was introduced as a new system to predict BIGV in open-pit mines accurately.

中文翻译：

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基于帝国主义竞争算法和M5规则的新技术预测露天矿爆破引起的地面振动

本文以爆炸引起的地面振动（BIGV）为主要目标，并提出了一种新的人工智能系统，以基于M5Rules和帝国主义竞争算法（ICA）的ICA–M5Rules技术高精度地预测BIGV。 。因此，ICA被认为是基于M5模型的规则以及修剪和平滑过程来优化M5Rules的。为了评估建议的ICA–M5Rules技术的有效性，开发了随机森林（RF），经典M5Rules和支持向量机（SVM）作为基准技术，以与建议的ICA–M5Rules技术进行比较。此外，两个现有的经验方程式分别用于根据实验数据集开发模型，以估计BIGV，以便与提出的ICA–M5Rules模型进行比较。越南的一个采石场的案例研究被用于基于125次爆炸事件的已开发（ICA–M5规则，M5规则，RF，SVM，经验）模型。平均绝对误差，均方根误差和系数的确定（R ²）被应用和计算以评估准确性，以及所开发模型的性能。调查结果表明，建议的ICA–M5Rules模型提供了最高的准确性。根据开发模型的结果适当地解决了主要目标。建立完善的ICA–M5Rules模型被引入作为一种可准确预测露天矿中BIGV的新系统。