Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Abstract

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

“…ese algorithms are used to optimize the performance of machine learning model to achieve a balance between model accuracy and model generalization. e employed metaheuristic approaches include symbiotic organisms search [42], particle swarm optimization [43,44], the forensic-based investigation optimization [45], equilibrium optimization [20], Harris hawks optimization [46], simulated annealing [47], social spider optimization [48,49], gray wolf optimization [38,50], teaching-learningbased algorithm [51], salp swarm algorithm [52,53], artificial bee colony [54], pigeon-inspired optimization [55], cuckoo search optimization [56], imperialist competitive algorithm [57], moth flame optimization [58], and cuckoo search algorithm [59]. ose previous works have demonstrated the effectiveness of metaheuristic algorithms in optimizing machine learning models and solving complex tasks in various application domains.…”

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

“…Since proposed [53], it has been used widely [54]- [56], such as solar energy [57]- [59], feature selection [60], drug design and discovery [61]. Furthermore, a large number of improved HHO variants have been presented, for example, hybrid HHO-based sine cosine mechanism [62], Nelder-mead driven HHO [63], generalized Gaussian distribution HHO [64], multi-objective HHO [65], mutation strategies-based HHO [66], diversification enriched HHO [58], Multi-population version [67] random forest model based-HHO [68]. In this study, the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer have been integrated to enhance and restore the search capability of the HHO.…”

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

“…It is noted that the process of fine-tuning a machine learning model can be formulated as a global optimization problem. In recent years, various metaheuristic approaches have been successfully employed, including monarch butterfly optimization [62,63], slime mould algorithm [64], moth search algorithm [65,66], Harris hawks optimization [67][68][69][70], differential flower pollination [71], symbiotic organisms search [72], Henry gas solubility optimization [73], and satin bowerbird optimizer [74]. As can be seen from the literature, there is an increasing trend of hybridizing metaheuristics and machine learning to tackle complex problems in the field of engineering.…”