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Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock

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  • Weixing Lin

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China
    Changsha Institute of Mining Research Co., Ltd., Changsha 410012, China)

  • Leilei Liu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Guoyan Zhao

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Zheng Jian

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

Accurate estimation of the elastic modulus ( E ) of rock is critical for the design of geotechnical projects such as mining, slopes, and tunnels. However, the determination of rock mechanical parameters usually involves high budget and time requirements. To address this problem, numerous researchers have developed machine learning models to estimate the E of rock. In this study, two novel hybrid ensemble learning models were developed to estimate the E of rock by optimizing the extreme gradient boosting (XGBoost) and random forest (RF) algorithms through the dwarf mongoose optimization (DMO) approach. Firstly, 90 rock samples with porosity, dry density, P -wave velocity, slake durability, and water absorption as input indicators were collected. Subsequently, the hyperparameters of XGBoost and RF were tuned by DMO. Based on the optimal hyperparameters configuration, two novel hybrid ensemble learning models were constructed using the training set (80% of the data). Finally, the performance of the developed models was evaluated by the coefficient of determination ( R 2 score), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF) on the test set (20% of the data). The results show that the DMO-RF model achieved the best comprehensive performance with an R 2 score of 0.967, RMSE of 0.541, MAE of 0.447, and VAF of 0.969 on the test set. The dry density and slake durability were more influential indicators than others. Moreover, the convergence curves suggested that the DMO-RF model can reduce the generalization error and avoid overfitting. The developed models can be regarded as viable and useful tools in estimating the E of rock.

Suggested Citation

  • Weixing Lin & Leilei Liu & Guoyan Zhao & Zheng Jian, 2023. "Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3886-:d:1238300
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    References listed on IDEAS

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    1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.
    2. Zhongyuan Gu & Miaocong Cao & Chunguang Wang & Na Yu & Hongyu Qing, 2022. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
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