Stability prediction of roadway surrounding rock using INGO-RF

Abstract

In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters, this study proposes an Improved Northern Gok algorithm (INGO) and Random Forest (RF) roadway surrounding rock stability prediction model. This model combines the improved INGO-RF based on the analysis of influencing factors of roadway surrounding rock stability. First, three strategies were employed to enhance the Northern Gob algorithm (NGO): logistic chaotic mapping, refraction reverse learning, and improved sine and cosine. Subsequently, INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF. Secondly, a data set consisting of 34 groups of roadway surrounding rock data is selected. The input indexes of the model include the roof strength, two-wall strength, floor strength, burial depth, roadway pillar width, ratio of direct roof thickness to mining height, and surrounding rock integrity. Meanwhile, surrounding rock stability is considered as the output index. Particle swarm optimization backpropagation neural network (PSO-BPNN), genetic algorithm optimization support vector machine (GA-SVM), Sparrow Search Algorithm optimization RF (SSA-RF) models were introduced to compare the predictive results with the INGO-RF model, and the results showed that: INGO-RF model has the best performance in the comparison of various performance indicators; compared with other models, the accuracy rate (Ac) in the test set has increased by 0.12-0.40, the accuracy rate (Pr) has increased by 0.07-0.65, and the recall rate (Re) has increased by 0.08-0.37; the harmonic mean (F₁-Score) of the recall rate increased by 0.08-0.52, the mean absolute error (MAE) decreased by 0.1428-0.4285, the mean absolute percentage error (MAPE) decreased by 7.15%-28.57 %, and the root mean square error (RMSE) decreased by 0.1565-0.3779; and finally, the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model. The results indicate that the predicted outcomes closely align with the actual results, demonstrating a certain level of reliability and stability, which can better meet the practical needs of engineering and avoid the occurrence of mine disasters.

Keywords

Roadway surrounding rock stability / Northern goshawk optimization (NGO) / Random forest (RF) / Prediction model / Model performance index

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 supported by the National Natural Science Foundation of China (No.52174188), and the Shanxi Basic Research Program (Free Exploration) Project of China (No.202203021222300).

References

[1] L.I. Qingfeng, Hao Wu, Qingqin Lu, et al., Research progress and intelligent control countermeasures of surrounding rock stability control of strong dynamic pressure roadway, Min. Eng. Res. 37 (2) (2022) 17-25.

[2] L.I. Haocheng, Xuanmin Song, Defu Zhu, et al., Method for parameter optimization design of entry retaining through cutting thick and hard main roof, Coal Eng. 53 (1) (2021) 94-99.

[3] N.R. Kerbati, L. Gadri, R. Hadji, et al., Graphical and numerical methods for stability analysis in surrounding rock of underground excavations, example of boukhadra iron mine N.E Algeria, Geotech. Geol. Eng. 38 (18) (2020) 2725-2733.

[4] L.I.U. Yang, Y.E. Yicheng, Xiaoyun Liu, et al., Stability forecast on surrounding rock of roadway based on unascertained clustering method, J. Saf. Sci. Technol. 13 (2) (2017) 56-61.

[5] M.A. Xinmin, Pan Chen, C.H.E.N. Chen, et al., Prediction of surrounding rock stability of coal roadway based on machine learning and its application, J. Min. Sci. Technol. 8 (2) (2023) 156-165.

[6] O. Bazaluk, M. Petlovanyi, S. Zubko, V. Lozynskyi, K. Sai, Instability assessment of hanging wall rocks during underground mining of iron ores, Minerals 11 (8) (2021) 858.

[7] A. Sikora, A. Zielonka, M.F. Ijaz, et al., Digital twin heuristic positioning of insulation in multimodal electric systems, IEEE Trans. Consum. Electron. (2024) 3370505.

[8] M. Wo zniak, M. Wieczorek, J. Siłka, BiLSTM deep neural network model for imbalanced medical data of IoT systems, Future Generat. Comput. Syst. (141) (2023) 489-499.

[9] Dongsheng Wang J.I.N. Xiao, Prediction of surrounding rock stability in coal roadway based on JADE-ELM method, Saf. Coal Mine 48 (11) (2017) 198-201.

[10] L.I.U. Yang, Prediction of Stability of Roadway Surrounding Rock Based on Concept Lattice and Probabilistic Neural network[D], Wuhan University of Science and Technologu, Wuhan, 2018.

[11] W.A.N.G. Qiang, Study on Early Warning Model of Surrounding Rock Stability of Mining Roadway Based on SVM[D], Xi’an: Xi’an University of Science and Technology, 2020.

[12] Liangshan Shao, Yu Zhou,Prediction on improved GSM-RFC model for cast surrounding rock stability classification of gateway, J. Liaoning Tech. Univ. 37 (3) (2018) 449-455.

[13] Z.H.A.O. Yan, Coupling Analysis of Bearing Structure and Rational Control in Deep Soft Rock roadway[D], Anhui University of Science and Technology, Huainan, 2017.

[14] L.E.I. Xianquan, Fuchen Liu, Qingwen Yan, et al., Research on damage zone and load-bearing structure characteristics of surrounding rock mass of roadway in complex strata with over buried depth and high in situ stress, Metal. Mine (8) (2023) 171-180.

[15] Pengbo Yang, Research on the Mechanism of Floor Heave and its Anchor Pile Control in Roadway with High Stress Soft floor[D], Taiyuan University of Technology, Taiyuan, 2022.

[16] Jie Yin, Research on Reasonanble Coal Pillar Width and Surrounding Rock Control of Roadway Driving along Goaf in Xutuan coalmine[D], Anhui University of Science and Technology, Huainan, 2020.

[17] Yujie Mei L.I. Yong Z.H.O.U. Wangfeng, et al., Dynamic data cleaning method of abnormal and missing data in a distribution network based on machine learning, Power Syst. Protect. Contr. 51 (7) (2023) 158-169.

[18] L.I. Chao, Z.H.A.O. Linhai, CACC-RF-based risk prediction of rutters indicating notch jamming failures, Railway J. 44 (6) (2022) 46-55.

[19] M. Dehghani, S. Hubalovsky, P. Trojovsky, Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems, IEEE Access 9 (2021) 162059-162080.

[20] L.I. Gang, K.O.N.G. Yacong, D.A.I. Yuanshuai, et al., Study on monitoring model for total nitrogen content in plow layer of cotton field based on field in-situ spectroscopy, Agric. Res. Arid Areas 41 (6) (2023) 273-280.

[21] Junlian Zhang, Research on GNGO-ELM Assembled Construction Cost Prediction Model Based on EPC mode[D], Hebei University of Engineering, Handan, 2023.

[22] Chao Yang, Zhifeng Guan, Yuzhu Liu, et al., A new method for predicting coal and gas outburst based on IAEFA-LSSVM, Mechan. Electr. Eng. Technol. 52 (2) (2023) 51-54.

[23] S.H.E.N. Mengyan, W.E.I. Wenshan, R.O.N.G. Xin, Short-term load forecasting based on ISSA-BiGRU-Attention, Mod. Comput. 28 (20) (2022) 61-64.

[24] Leifu Gao R.O.N. G. Xuejiao, Improved YSGA algorithm combining declining strategy and fuch chaotic mechanism, J. Front. Comput. Sci. Technol. 15 (3) (2021) 564-576.

[25] L.I. Jinlian, Q.U.A.N. Lingxiang, C.U.I. Guimei, et al., Sparrow search algorithm combining Sine-Cosine and Cauchy mutation, Comput. Eng. Appl. 58 (3) (2022) 91-99.

[26] L.I.N. Feng, Peng Guo, Xubin Liu, Wind turbine blade surface damage identification based on blade surface dirt pretreatment and CNN, J. Chin. Soc. Power Eng. 40 (12) (2020) 975-981.

[27] L.I.U. Bing, Y.E. Chengxu, Fine-grained classification model of lung disease for imbalanced data, J. Graph. 44 (3) (2023) 513-520.

[28] S.H.I. Yongkui, Jingyu Zhang, Wenzheng Wang, et al., Analysis of prediction method of stability of surrounding rock in mining roadway, Coal Technol. 34 (7) (2015) 46-49.

[29] Rujiu Zhang Y.A.N. Lei, Xuhui He, et al., Research on wind velocity prediction models based on hybrid methods, J. Railw. Sci. Eng. 17 (7) (2020) 1630-1636.