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        戴   汀1 ,2 ,  李向國1 ,2 ,  王    凱3

 

( 1 .  道路與鐵道工程安全保障省部共建教育部重點實驗室（石家莊鐵道大學），石家莊 050043 ;   2.  石家莊鐵道大學 土木工程學院 ，石家莊 050043 ;  3 .   中建三局一公司基礎分公司 ，武漢 430064)

摘   要 ：針對傳統方法難以快速精準預測隴中地區黃土濕陷性的問題 ，開展基于支持向量機回歸的黃土濕 陷性預測研究。研究依托隴中地區某鐵路項目 ，于人工探井中采集樣土進行室內土工試驗 ， 以獲取土的相關物 性指標數據。通文獻分析法和數理分析 ，探究黃土濕陷系數與各物性指標的相關性。在此基礎上 ，基于支持 向量機 ，采用高斯核函數構建黃土濕陷性預測模型。以 45 組試驗數據作為訓練集 ，9 組作為測試集進行誤差分 析。結果表明：預測模型訓練集平均絕對誤差為 0. 003 、平均偏差誤差為 0. 001 、均方根誤差為 0. 015 9 ;  測試集 平均絕對誤差為 0. 013 2 、平均偏差誤差為 0. 001 8 、均方根誤差為 0. 015 ,  表明該模型預測精度良好 ，可有效預 測隴中地區黃土濕陷系數 ，為實際工程中黃土濕陷性的預測提供了可靠的新方法。

關鍵詞：黃土濕陷性 ；物性指標 ；相關性 ；支持向量機 ；預測模型

中圖分類號：TU444           文獻標志碼：A   

         

文章編號： 1005- 8249   (2025)  03- 0073- 05 

DOI:10. 19860/j.cnki.issn1005 - 8249.2025 .03 .014

DAI Ting1 , 2  ,  LI Xiangguo1 , 2  ,  WANG Kai3

(1. Key Laboratory of Road and Railway Engineering Safety and security, Ministry of Education (Shijiazhuang Railway University), Shijiazhuang 050043, China;

2. School of Civil Engineering, Shijiazhuang University of Railways, Shijiazhuang 050043, China

3. Basic Branch of China Construction Third Bureau One Company, Wuhan 430064, Hubei)

 

Abstract:   To address the challenges in rapid and accurate prediction of loess collapsibility in Central Gansu using conventional methods, a support vector machine regression-based predictive model was developed. Leveraging data from a railway project in this region, soil samples collected from manually excavated test pits underwent comprehensive laboratory geotechnical testing to obtain key physical property indices.Correlations between loess collapse coefficients and physical indices were systematically investigated through literature analysis and mathematical statistics. A Gaussian kernel function-based SVM prediction model was subsequently established. The model was trained with 45 experimental datasets, while 15 datasets were utilized for validation and error analysis. Results demonstrate that the model achieves a mean absolute error (MAE) of 0.003, mean bias error (MBE) of 0.001, and root mean square error (RMSE) of 0.0159 on the training set. For the testing set, performance metrics indicate MAE=0.0132, MBE=0.0018, and RMSE=0.015. These findings confirm the model's high predictive accuracy for estimating loess collapse coefficients in Central Gansu, providing a reliable novel methodology for practical engineering applications.

Key words:   loess collapsibility ; physical property index ; correlation ; support vector machine ; prediction model

基金項目： 國家自然科學基金  (52378453) 。

作者簡介：戴   汀  (1998—) ,  男 ，碩士研究生 ，研究方向： 黃土濕陷性。

通信作者：李向國  (1973—) ,  男 ，博士 ，教授 ，研究方向：鐵路線路結構設計 、施工等方面。

收稿日期：2025 - 01 - 18