Goaf risk prediction based on IAOA-SVM and numerical simulation: A case study

Abstract

In regard to goaf risk prediction, due to the low accuracy and single prediction method, this study proposes a method that combines the improved arithmetic optimization algorithm (IAOA) - support vector machines (SVM) with GoCAD-FLAC^3D numerical simulation. Thus, goaf risk is comprehensively predicted. From the perspectives of geological and engineering conditions, eight factors that affect goaf stability and 176 sets of sample data were determined. We utilized eight influencing factors such as rock mass structure, geological structure, and goaf burial depth as inputs, and the goaf risk level as the output. Moreover, an IAOA-SVM goaf risk prediction model was established. The 30 goaf areas of Yangla Copper Mine in Yunnan Province were selected as the research subject. First, the rationality of mechanical parameter values in the numerical model was verified using the parameter inversion method. Second, based on the GoCAD-FLAC^3D numerical simulation method, the goaf risk analysis in Yangla Copper Mine was performed. Subsequently, using numerical simulation verification, the goaf filling effect was analyzed. Finally, the prediction results of the IAOA-SVM model were compared with that of other intelligent algorithms. The results indicate that the numerical simulation results of the GoCAD-FLAC^3D model are consistent with those of IAOA-SVM and the actual results, which further verifies the effectiveness and superiority of the IAOA-SVM prediction model. Therefore, an innovative approach for goaf risk prediction is developed.

Keywords

Rock mechanics / Goaf risk prediction / IAOA / SVM / Numerical simulation

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded by the National Science Foundation of China (Grant No. 51934003) and the Major Science and Technology Special Project of Yunnan Province, China (Grant No. 202202AG050014). The supports are gratefully acknowledged.

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