CAJ | 학술논문

隧道洞口处多为软弱岩或浮土，稳定性差，地表位移监测成为判断洞口稳定性的重要手段，因此仰坡沉降变形预测显得格外重要。鉴于仰坡沉降变形具有很强的非线性特征，选取BP神经网络对仰坡的沉降变形进行预测，并验证其可行性，进而利用BP神经网络扩大沉降变形监测的样本。在此基础上，再利用R/S分析对新的监测样本进行重标极差分析，分别得到隧道仰坡沉降-时间序列和变形速率-时间序列的Hurst指数，并结合两项指数确定了隧道仰坡沉降变形的趋势，为判断仰坡的稳定性及治理提供了有力依据。
수도동구처다위연약암혹부토，은정성차，지표위이감측성위판단동구은정성적중요수단，인차앙파침강변형예측현득격외중요。감우앙파침강변형구유흔강적비선성특정，선취BP신경망락대앙파적침강변형진행예측，병험증기가행성，진이이용BP신경망락확대침강변형감측적양본。재차기출상，재이용R/S분석대신적감측양본진행중표겁차분석，분별득도수도앙파침강-시간서렬화변형속솔-시간서렬적Hurst지수，병결합량항지수학정료수도앙파침강변형적추세，위판단앙파적은정성급치리제공료유력의거。
There are much weak weathered rockmass and topsoil in tunnel slope, its’ poor stability makes settlement monitoring much more important to decide the stability of tunnel entrance. Therefore the settlement deformation prediction in tunnel slope is necessary. In view of strong non-linear characteristics of the slope settlement deformation, this paper selects BP neural network to predict deformation of the slope and verifies the feasibility, and then uses the BP neural network to expand the sample of settlement monitoring. Based on the above, the new monitoring samples are analyzed by R/S analysis, and Hurst index of settlement-time sequence and deformation rate-time series of the overlaying slope is achieved. Then the two Hurst indexes are combined to determine settlement deformation trend of the front slope in tunnel, which provides a strong basis for judging the slope stability and governance.