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(中鐵十四局集團(tuán)第二工程有限公司， 泰安 271000)

［摘要］邊坡的穩(wěn)定性預(yù)測是道路邊坡安全性評(píng)判的關(guān)鍵，且及時(shí)、準(zhǔn)確的預(yù)測可以有效地預(yù)防邊坡破壞災(zāi)害的發(fā)生。采用GM(1,N)模型與RBF神經(jīng)網(wǎng)絡(luò)模型相結(jié)合的方式，建立一種基于GM-RBF組合的高路塹邊坡變形預(yù)測分析模型。結(jié)合高速公路高路塹邊坡工程實(shí)例，通過對(duì)比GM(1,5)模型、RBF神經(jīng)網(wǎng)絡(luò)模型和GM-RBF組合模型的邊坡安全系數(shù)預(yù)測結(jié)果來分析GM-RBF組合模型的可行性。結(jié)果表明，GM-RBF組合模型比單一模型更能抵抗預(yù)測數(shù)據(jù)序列中存在的波動(dòng)性；較于GM(1,5)模型和RBF神經(jīng)網(wǎng)絡(luò)模型，GM-RBF組合模型預(yù)測的邊坡安全系數(shù)平均絕對(duì)誤差分別降低了64.6%和45.8%，邊坡安全系數(shù)均方根誤差分別降低了66.7%和45.2%，邊坡安全系數(shù)相對(duì)均方誤差也分別降低了58.3%和38.7%；采用GM-RBF組合模型對(duì)邊坡穩(wěn)定性進(jìn)行預(yù)測能夠保持良好的精度。 

［關(guān)鍵詞］路基工程；邊坡穩(wěn)定性；安全系數(shù)；灰色算法；神經(jīng)網(wǎng)絡(luò)；GM-RBF組合模型 

中圖分類號(hào)：TU753, U417-1 文獻(xiàn)標(biāo)識(shí)碼：A文章編號(hào)：1002-848X(2021)20-0140-06

Study on stability prediction of high cutting slope based on GM-RBF combination model 

WANG  Pengfei 

(China Railway 14th Bureau Group 2nd Engineering Co., Ltd., Tai’an 271000， China) 

Abstract: Prediction of slope stability is the critical point of the road slope safety evaluation, and timely and accurate prediction can effectively prevent the occurrence of slope damage disasters. The analysis model of deformation prediction for high cutting slope based on GM-RBF combination mode was established by adopting the combination of GM(1,N) model and RBF neural network model. Combining the high cutting slope engineering example of the expressway, the feasibility of GM-RBF combination mode was analyzed by comparing the prediction results of the slope safety factor of GM(1,5) model, RBF neural network model and GM-RBF combination model. The results show that GM-RBF combination model can resist the volatility in the predictive data series more effectively than single model. Comparing to GM (1,5) model and RBF neural network model, the average absolute error of slope safety factor predicted by GM-RBF combination model is reduced by 64.6% and 45.8% respectively, the root mean square error of slope safety factor is reduced by 66.7% and 45.2% respectively, and the relative mean square error of slope safety factor is reduced by 58.3% and 38.7% respectively. Using GM-RBF combination model to predict slope stability can maintain good accuracy. 

Keywords:subgrade engineering； slope stability； safety factor； grey algorithm； neural network； GM-RBF combination model

*天津市交委科技計(jì)劃項(xiàng)目（2021-24）。

 作者簡介：王鵬飛，學(xué)士，高級(jí)工程師，Email: wpfzt1982@126.com。