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龍   凱¹  ,  鄧經(jīng)祥²  ,  李鴻釗³  ,  惠    冰³  ,  石亞強(qiáng)²  ,  張文俊³

 

( 1 .  濟(jì)寧市公路事業(yè)發(fā)展中心 ，濟(jì)寧 272008 ;  2.  葛洲壩集團(tuán)交通投資有限公司 ，宜昌 443005 ;

3 .  山東省交通科學(xué)研究院 ，濟(jì)南 250031)

摘   要：路基壓實(shí)度與道路的質(zhì)量密切相關(guān)，直接影響工程的穩(wěn)定性和耐久性。為了建立路基壓實(shí)度預(yù)測模型，開展路基壓實(shí)度現(xiàn)場試驗(yàn)，通過控制碾壓次數(shù)、碾壓速度和含水率的試驗(yàn)方法對壓實(shí)度的影響規(guī)律 ，采  用非線性回歸、決策樹、支持向量機(jī) 、神經(jīng)網(wǎng)格和 XGBoost 等算法建立了6 個(gè)壓實(shí)度預(yù)測模型，對其預(yù)測性能評價(jià)。結(jié)論表明：路基壓實(shí)度與碾壓次數(shù)呈正比，而與碾壓速度呈反比。當(dāng)含水率處于最優(yōu)含水率時(shí)，路基壓實(shí)  度最大。含水率 、碾壓次數(shù) 、碾壓速度對壓實(shí)度的影響依次降低；支持向量機(jī)模型對于訓(xùn)練集預(yù)測效果較差， 決策樹模型對于預(yù)測集的預(yù)測效果較差，不適用于路基壓實(shí)度的預(yù)測；冪函數(shù)和對數(shù)函數(shù)兩種非線性回歸模型， 神經(jīng)網(wǎng)絡(luò)和 XGBoost 兩種機(jī)器學(xué)習(xí)模型適用于路基壓實(shí)度的預(yù)測。機(jī)器學(xué)習(xí)的預(yù)測性能要高于非線性回歸模型。

關(guān)鍵詞：路基壓實(shí)度 ；現(xiàn)場試驗(yàn) ；預(yù)測模型 ；性能評價(jià) ；機(jī)器學(xué)習(xí)

中圖分類號：U447           

文獻(xiàn)標(biāo)志碼：A            

文章編號： 1005- 8249   (2025)  01- 0124- 06 

DOI:10. 19860/j.cnki.issn1005 - 8249.2025 .01 .023

 

LONG Kai ¹  ,  DENG Jingxiang² ,  LI Hongzhao³  ,  HUI Bing³  ,  SHI Yaqiang² ,  ZHANG Wenjun³

(1 . Ji ’ning Highway Devlopment Center ,  Ji ’ning 272008 ,  China;

2. Gezhouba Group Transportation Investment Co. ，Ltd. ，Yichang 443005 ,  China;

3 . Shandong Transportation Institute ,  Ji ’nan 250031 ,  China)

Abstract: Subgrade compaction is closely related to the quality of the road and directly affects the stability and durability of the project. In order to establish the prediction model of subgrade compaction, carry out the field test of subgrade compaction, through the control of the number of rolling, rolling speed and water content of the test method on the compaction of the law, the use of nonlinear regression, decision tree, support vector machine, neural lattice and XGBoost algorithms to establish a six compaction prediction model, and its prediction performance evaluation. The conclusion shows:The subgrade compaction degree is positively proportional to the number of rolling times and inversely proportional to the rolling speed. When the water content is at the optimal water content, the subgrade compaction is maximum. The effects of water content, number of rolling times and rolling speed on compaction are reduced in order;the support vector machine model has a poor prediction effect on the training set, and the decision tree model has a poor prediction effect on the prediction set, which is not applicable to the prediction of subgrade compaction; the two nonlinear regression models of the power function and the logarithmic function, and the two machine learning models of the neural network and the XGBoost are applicable to the prediction of the compaction degree of the subgrade. . The prediction performance of machine learning is higher than the nonlinear regression model.

Key words: subgrade compaction degree; field test; predictive modeling; performance evaluation; machine learning

基金項(xiàng)目： 山東省交通運(yùn)輸科技項(xiàng)目  (2023B45) 。

作者簡介：龍   凱  (1988—) ,  男 ，碩士，工程師，研究方向：從事公路管理工作。

通信作者：李鴻釗  (1998—) ,  男 ，碩士，助理工程師 ，研究方向：路基壓實(shí)。

收稿日期：2024 - 03 - 26