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儀曉立1 , 王振軍2, 侯向陽1 ,  惠冰2, 孫巍1 , 張旭2, 苗鑫2

(1. 中鐵一局集團(tuán)建設(shè)安裝工程有限公司，陜西 西安 710000 ;

2. 山東省交通科學(xué)研究院， 山東 濟(jì)南 250104)

摘 要：砂土液化導(dǎo)致地基承載力下降，合理判別砂土液化程度對防治地基下沉等災(zāi)害具有重要意義。借鑒機(jī)器學(xué)習(xí)方法，選取30組砂土液化數(shù)據(jù)樣本，建立粒子群算法改進(jìn)最小二乘支持向量機(jī)砂土液化判別模型，并與SVM砂土液化判別模型和BP砂土液化判別模型進(jìn)行了對比分析。結(jié)果表明：LSSVM 模型通過PSO算法優(yōu)化后確定正則化參數(shù)為323 . 125 247 535 、核參數(shù)為 1 . 015 053 246 5。對于15組訓(xùn)練樣本，PSO-LSSVM砂土液化判別模型和SVM 砂土液化判別模型回判準(zhǔn)確率為100% , BP砂土液化判別模型回判準(zhǔn)確率為93.3%; 對于5組測試樣本，PSO - LSSVM砂土液化判別模型預(yù)測準(zhǔn)確率為100% , 而SVM砂土液化判別模型和BP 砂土液化判別模型預(yù)測準(zhǔn)確率為80%; 在黃河流域砂土液化預(yù)測中，PSO - LSSVM 砂土液化判別模型具有更高的預(yù)測精度，可指導(dǎo)工程技術(shù)人員預(yù)測砂土狀態(tài)制定防治措施。

關(guān)鍵詞：粒子群算法；支持向量機(jī)；砂土液化；判別預(yù)測

中圖分類號：TU435 

文獻(xiàn)標(biāo)志碼：A 

文章編號：1005- 8249 (2024) 03- 0050- 05 

DOI：10.19860/j.cnki.issn1005-8249.2024.03.011

YI Xiaoli1 , WANG Zhenjun2, HOU Xiangyang1, HUI Bing2, SUN Wei 1, ZHANG Xu2, MIAO Xin2

1. China Railway First Group Building & Installation Engineering Co. ，LTD. ，Xi ’an 710000 , China;

2. Shandong Transportation Institute, Jinan 250104, China

Abstract : The liquefaction of sand soil causes the bearing capacity of the foundation to decrease. Reasonable judgment of the degree of sand liquefaction is of great significance to prevent and control disasters such as foundation subsidence. Drawing on machine learning methods , 30 sets of sand liquefaction data samples were selected to establish a sand liquefaction discrimination model improved by particle swarm optimization algorithm , and compared with SVM and BP sand liquefaction discrimination model. The results show that the LSSVM model is optimized by the PSO algorithm and determines the regularization parameter to be 323 . 125 247 535 and the kernel parameter to be 1 . 015 053 246 5 . For 15 sets of training samples , the PSO - LSSVM sand liquefaction discrimination model and SVM sand liquefaction discrimination model have a return accuracy of 100% , and the BP sand liquefaction discrimination model has a return accuracy of 93. 3% . For the five groups of test samples , the prediction accuracy of the PSO - LSSVM sand liquefaction discrimination model was 100% , while the prediction accuracy of the SVM sand liquefaction discrimination model and the BP sand liquefaction discrimination model was 80% . In the prediction of sand liquefaction in the Yellow River Basin , the PSO - LSSVM sand liquefaction discrimination model has higher prediction accuracy and can guide engineering and technical personnel to predict the state of sand and formulate prevention and control measures.

Keywords : particle swarm algorithm; support vector machine; sand liquefaction; discriminant prediction

*基金項目： 山東省交通運(yùn)輸科技計劃項目 (2023B46) 。

作者簡介：儀曉立 (1975—) , 男，本科，高級工程師，研究方向：土木工程。 

收稿日期：2023-10-06