The focus of the study is to examine the undrained behavior of twin circular tunnels in anisotropic and nonhomogeneous clays. To consider the effect of anisotropic soil, the popular anisotropic undrained shear (AUS) failure criteria are adopted in the study while the nonhomogeneous behavior is represented by linearly increasing strength with depth. Using Broms and Bennermarks' stability number, this study investigates the dependence of the undrained stability number N on four dimensionless input parameters, namely the isotropic ratio (re), the undrained shear strength gradient (rho D/suTC0), the cover depth ratio (C/D), and the spacing ratio (S/D). The effects of these four design parameters on the failure mechanism are also examined graphically. After being verified with previously published works, the comprehensive 1080 numerical results are then utilized as the dataset to create several machine learning models, including artificial neural network (ANN), support vector machine (SVM), and multivariate adaptive regression splines (MARS). The evaluating process by optimizing hyper-parameters reveals that the MARS model is a top competitor, providing considerable regression accuracy with a simple predictive function. The sensitivity analysis has also uncovered that both rho D/suTC0 and C/D have significant influences on the undrained stability number N, while comparing to re and S/D. The present study would provide many practical insights to the problem of twin circular tunnels in anisotropic and nonhomogeneous clays.

PurposeThis study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.Design/methodology/approachThis research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.FindingsThis method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.Originality/valueCombining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

冰湖溃决不仅对财产和基础设施具有破坏性，而且对当地居民也构成极大威胁。冰湖溃决的预测和风险评估对于预防和减轻灾害影响至关重要。文中提出了一个冰湖溃决的预测模型，强调选取容易获得的预测因子。以喜马拉雅山地区的48个冰湖为样本，使用地理探测器检测4个选定的预测因子：母冰川面积、冰舌坡度、冰湖面积和坝顶宽度。结果显示：冰舌坡度q值最大，为0.334 2。在交互作用检测器中，母冰川面积和冰舌坡度在交互作用后有最高的解释力，为0.684 4。这表明：与冰湖和冰碛坝相比，母冰川对冰湖状态的影响更大。在利用SVM(Support Vector Machine，支持向量机)构建的冰湖溃决预测模型中，验证集和测试集的准确率分别为83.33%和87.5%。研究为喜马拉雅地区未来的灾害管理提供了相应参考。

Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e.,Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Nino-southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April-October) and the water year, which is from October of the previous year to September of the current year for a period from 1955-2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin. (C) 2013 American Society of Civil Engineers.