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Analysis of feature importance to retaining wall deformation of excavation using interpretable machine learning model
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Yadong LIU1, 2, 3, Xian LIU4, Hesong HU2, **, Hang CHEN1, Shengfang QIAO1
China Safety Science Journal | 2025, 35(4) : 110 - 119
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China Safety Science Journal | 2025, 35(4): 110-119
Safety engineering technology
Analysis of feature importance to retaining wall deformation of excavation using interpretable machine learning model
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Yadong LIU1, 2, 3, Xian LIU4, Hesong HU2, **, Hang CHEN1, Shengfang QIAO1
Affiliations
  • 1 Guangzhou Institute of Building Science Group Co.,Ltd.,Guangzhou Guangdong 510440,China
  • 2 Guangzhou Construction Engineering Co.,Ltd.,Guangzhou Guangdong 510030,China
  • 3 School of Civil Engineering and Transportation,South China University of Technology,Guangzhou Guangdong 510641,China
  • 4 School of Civil Engineering,Sun Yat-sen University,Guangzhou Guangdong 510275,China
Published: 2025-04-28 doi: 10.16265/j.cnki.issn1003-3033.2025.04.0893
Outline
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In order to improve the interpretability of excavation deformation prediction,this study developed an interpretable machine-learning model aimed at predicting the deformation of excavation retaining walls. A comprehensive analysis was conducted to evaluate the influence of different feature variables on the prediction outcomes. Firstly,a large number of excavation support structure parameters were used as a dataset,and 80% of the dataset was used to build a prediction model for the maximum lateral deflection of the retaining wall using the XGBoost (eXtreme Gradient Boosting)model. Then,the model was tested based on the remaining 20% of the dataset,and the accuracy of the model was evaluated by four indicators,i.e.,the coefficient of determination,bias factor,mean absolute percentage error,and root mean square error. Finally,combined with the XGBoost model,the SHAP(SHapley Additive exPlanations) method was applied to complete the global explanation of the excavation feature variables,the partial analysis of individual samples,and the analysis of interaction effects of feature variables. The results show that the proposed method can provide both global and local explanations for the deformation prediction of excavation. At the global level,it not only provides the importance ranking of feature variables,but also gives the distribution of SHAP values. At the local level,the deformation prediction results of individual samples are decomposed into the base value and the contribution of each feature variable,which can quantify the impact of individual feature variables.

interpretability  /  machine learning model  /  excavation  /  retaining wall deformation  /  influence factor  /  feature variable
Yadong LIU, Xian LIU, Hesong HU, Hang CHEN, Shengfang QIAO. Analysis of feature importance to retaining wall deformation of excavation using interpretable machine learning model[J]. China Safety Science Journal, 2025 , 35 (4) : 110 -119 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.0893
Year 2025 volume 35 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.04.0893
  • Receive Date:2024-12-30
  • Online Date:2025-07-05
  • Published:2025-04-28
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History
  • Received:2024-12-30
  • Revised:2025-02-19
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Affiliations
    1 Guangzhou Institute of Building Science Group Co.,Ltd.,Guangzhou Guangdong 510440,China
    2 Guangzhou Construction Engineering Co.,Ltd.,Guangzhou Guangdong 510030,China
    3 School of Civil Engineering and Transportation,South China University of Technology,Guangzhou Guangdong 510641,China
    4 School of Civil Engineering,Sun Yat-sen University,Guangzhou Guangdong 510275,China
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表12种不同金属材料的力学参数

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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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