Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models
Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models
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DOI：
10.1016/j.soildyn.2020.106390
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发表时间：
2020-12
影响因子：
4
通讯作者：
Jian Zhou;P. G. Asteris;D. J. Armaghani;B. Pham
中科院分区：
文献类型：
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作者：
Jian Zhou;P. G. Asteris;D. J. Armaghani;B. Pham
本研究旨在比较两种机器学习技术的性能，这两种技术可以揭示输入变量和目标变量之间的关系，并预测采石场爆破引起的地面振动（峰值粒子速度，PPV）。为此，开发了随机森林（RF）模型和贝叶斯网络（BN）模型。在开发这些模型之前，为了说明提出新智能系统的必要性，提出了一个新的经验方程，使用每次延迟的最大装药量和距爆炸面的距离。结果证实确实需要开发具有更多输入参数的智能系统。因此，应用特征选择（FS）模型来降低数据的维数并去除不相关的数据。该技术的输出设置了五个参数：孔深、功率因数、堵塞、每次延迟的最大装药量以及距爆炸面的距离，作为预测 PPV 所需的最重要的模型输入。在构建FS-BN和FS-RF模型并在不同条件下（即计算成本、准确性和鲁棒性）进行比较后，发现所开发的FS-RF模型可以作为爆破环境问题领域的新模型引入。 FS-RF模型的准确度相当高；训练和测试阶段的准确率分别为 92.95% 和 90.32%，而 FS-BN 模型的训练和测试准确率分别为 92.95% 和 87.09%。因此，两种开发的混合模型都可以有效地揭示输入变量和目标变量之间的关系。
The present study aims to compare the performance of two machine learning techniques that can unveil the relationship between the input and target variables and predict the ground vibration (peak particle velocity, PPV) due to quarry blasting. To this end, a Random Forest (RF) model and a Bayesian Network (BN) model were developed. Before developing these models, and in order to illustrate the necessity of proposing new intelligent systems, a new empirical equation is proposed, using maximum charge per delay and distance from the blast-face. The results confirm that there is indeed a need to develop intelligent systems with more input parameters. Thus, a Feature Selection (FS) model is applied to decrease the dimensionality of data and remove the irrelevant data. The outputs of this technique set five parameters, hole depth, power factor, stemming, maximum charge per delay and distance from the blast-face, as the most important model inputs necessary to predict PPV. After constructing FS-BN and FS-RF models and comparing them under different conditions (i.e., computational cost, accuracy and robustness), it is found that the developed FS-RF model can be introduced as a new model in the field of blasting environmental issues. The accuracy level of the FS-RF model is quite high; 92.95% and 90.32% for the train and test stages, respectively, while 92.95% and 87.09% accuracy is calculated for train and test of the FS-BN model. Thus, both developed hybrid models can effectively unveil the relationships between the input and target variables.