Interpretable Prediction of Rock Strength: A Study Based on the COA⁃LightGBM⁃SHAP Framework

Zhang Yan; Yang Qilai; Luo Jing; Liu Yabin; Jian Huailei; Zeng Peng; Li Tianbin

doi:10.3799/dqkx.2026.261

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Interpretable Prediction of Rock Strength: A Study Based on the COA⁃LightGBM⁃SHAP Framework

更新时间：2026-10-15

- Interpretable Prediction of Rock Strength: A Study Based on the COA⁃LightGBM⁃SHAP Framework
- Earth Science Vol. 51, Issue 9, Pages: 3433-3444(2026)
- 作者机构：
  
  1.成都理工大学地质灾害防治与地质环境保护全国重点实验室，四川成都 610059
  2.陕西兴通监理咨询有限公司，陕西西安 710061
- 作者简介：
- 基金信息：
- DOI：10.3799/dqkx.2026.261
  CLC： P642
- Received：26 January 2026，
  
  Published：30 September 2026
- 稿件说明：
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张岩，杨奇来，罗璟，刘亚斌，简怀磊，曾鹏，李天斌，2026.岩石强度的可解释性预测：基于COA⁃LightGBM⁃SHAP框架.地球科学，51（9）：3433-3444.

Zhang Yan，Yang Qilai，Luo Jing，Liu Yabin，Jian Huailei，Zeng Peng，Li Tianbin，2026.Interpretable Prediction of Rock Strength： A Study Based on the COA⁃LightGBM⁃SHAP Framework.Earth Science，51（9）：3433-3444.
张岩，杨奇来，罗璟，刘亚斌，简怀磊，曾鹏，李天斌，2026.岩石强度的可解释性预测：基于COA⁃LightGBM⁃SHAP框架.地球科学，51（9）：3433-3444. DOI： 10.3799/dqkx.2026.261.

Zhang Yan，Yang Qilai，Luo Jing，Liu Yabin，Jian Huailei，Zeng Peng，Li Tianbin，2026.Interpretable Prediction of Rock Strength： A Study Based on the COA⁃LightGBM⁃SHAP Framework.Earth Science，51（9）：3433-3444. DOI： 10.3799/dqkx.2026.261.

摘要

岩石单轴抗压强度（UCS）是表征岩体力学特性的核心参数，在工程稳定性分析中具有重要作用.然而，由于岩石材料的复杂性，高效且精确地确定UCS仍是一项重大挑战.本研究通过将Light Gradient Boosting Machine（LightGBM）与Coati优化算法（COA）相结合，开发了一个稳健的预测框架.基于包含273组岩石样本的综合数据库，利用COA系统地优化了LightGBM模型的超参数.研究结果表明，COA-LightGBM模型在岩石单轴抗压强度预测中表现卓越，其RMSE、

R²

和MAE值分别为20.48、0.83和15.24.对比试验结果表明，COA-LightGBM模型在岩石单轴抗压强度预测上的性能显著优于最近邻（KNN）、决策树（DT）、随机森林（RF）等对比模型.此外，引入了Shapley Additive Explanations（SHAP）方法来阐明内部决策机制，并弥合“黑箱”模型与物理可解释性之间的差距.特征分析表明，孔隙率（

）是控制岩石强度的最关键因素.这项研究不仅证实了COA优越的优化能力，还为工程实践中岩石力学参数的快速评估提供了科学且可解释的解决方案.

Abstract

Uniaxial compressive strength (UCS) is a fundamental parameter for characterizing the mechanical behavior of rock masses which is essential for geotechnical stability analysis. However

the efficient and precise determination of UCS remains a significant challenge due to the complexity of rock materials. This study develops a robust predictive framework by integrating the Light Gradient Boosting Machine (LightGBM) with the Coati Optimization Algorithm (COA). Based on a comprehensive database of 273 rock samples

the COA is utilized to systematically optimize the hyperparameters of the LightGBM model. The results indicate that the proposed COA-LightGBM model exhibits exceptional predictive accuracy

achieving an RMSE of 20.48

of 0.83

and an MAE of 15.24. Comparative analyses demonstrate that the COA-LightGBM model significantly outperforms conventional models such as K-Nearest Neighbors

Decision Trees

and Random Forests. Furthermore

the Shapley Additive Explanations (SHAP) method is introduced to elucidate the internal decision-making mechanism and bridge the gap between “black-box” models and physical interpretability. Feature analysis reveals that porosity (

) is the most critical factor governing rock strength. This research not only validates the superior optimization capability of COA but al

so provides a scientific and interpretable solution for the rapid assessment of rock mechanical parameters in engineering practice.

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