Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model

YUAN Congxiang; LIU Zhixiang; YANG Xiaocong; GUO Jinfeng; WAN Chuanchuan; XIONG Shuai; LIU Weijun

doi:10.11858/gywlxb.20230668

Volume 37 Issue 5

Nov 2023

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Chinese Journal of High Pressure Physics > 2023 > 37(5): 054201.

YUAN Congxiang, LIU Zhixiang, YANG Xiaocong, GUO Jinfeng, WAN Chuanchuan, XIONG Shuai, LIU Weijun. Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model[J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668

Citation:

YUAN Congxiang, LIU Zhixiang, YANG Xiaocong, GUO Jinfeng, WAN Chuanchuan, XIONG Shuai, LIU Weijun. Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model[J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668

Citation:

YUAN Congxiang, LIU Zhixiang, YANG Xiaocong, GUO Jinfeng, WAN Chuanchuan, XIONG Shuai, LIU Weijun. Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model[J]. Chinese Journal of High Pressure Physics, 2023, 37(5): 054201. doi: 10.11858/gywlxb.20230668

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Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model

doi: 10.11858/gywlxb.20230668

1.
School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
2.
BGRIMM Technology Group, Beijing 102628, China
3.
National Center for International Joint Research on Green Mental Mining, Beijing 102628, China
4.
Sinosteel Maanshan General Institute of Mining Research Co. Ltd., Maanshan 243000, Anhui, China

Received Date: 22 May 2023
Rev Recd Date: 12 Jun 2023
Accepted Date: 14 Jul 2023

Available Online: 28 Sep 2023

Issue Publish Date: 07 Nov 2023

Abstract

Abstract

The uniaxial compressive strength of cemented paste backfill (CPB), as an important indicator of their mechanical properties, is usually determined by traditional mechanical tests. In the proposed model, the whale optimization algorithm (WOA) with global optimization capacity was used to tune the hyperparameters of the extreme gradient boosting (XGBoost) model. Taking the 80 sets of data obtained from the filling slurry ratio test of a lead-zinc mine as the database, the solid mass fraction, cement content, tailings content as well as curing age, were selected as input variables and the uniaxial compressive strength of the filling body as an output variable. XGBoost, random forest (RF) and WOA-RF models were constructed to compare with the WOA-XGBoost model. The results indicates that the hybrid WOA-XGBoost model (Its determination coefficient is 0.965 0, the root mean square error is 0.207 4, and the mean absolute error is 0.170 3) performs rather better than the individual XGBoost model (Its determination coefficient is 0.897 1, the root mean square error is 0.408 4, and the mean absolute error is 0.246 7). Compared with other models, the WOA-XGBoost model exhibits the highest prediction accuracy, contributing to the design and ratio optimization of cemented paste backfill materials.
- cemented paste backfill,
- uniaxial compressive strength,
- prediction,
- extreme gradient boosting model,
- whale optimization algorithm

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References

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Strength Prediction of Cemented Paste Backfill Body Based on WOA-XGBoost Model

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