基于等效塑性应变的三维边坡滑面搜索

边坡三维临界滑面的定位问题是边坡稳定性分析中尚未得以很好解决的一个重要问题。注意到临界滑面是由沿深部方向上的等效塑性应变的极大点所组成这一现象,在利用强度折减法将边坡带入临界平衡状态后,通过在水平面上设置一系列的垂直直线,并找出每条直线上的等效塑性应变最大值的位置,就得到了三维边坡滑面上离散点的分布。最后,采用被称为薄板光顺样条（thin-plate smoothing spline）函数的插值方法对滑面上的离散点进行了插值光顺处理,在不指定边坡滑动模式和滑面形状的前提下就可以得到三维边坡的临界滑面。通过在三维凸型边坡中的应用,体现了该方法在确定三维边坡临界滑面上的优越性。     

指数平滑法及其在滑坡预报中的应用

指数平滑法是一种非统计性的预报方法，它具有计算过程简单，适应性好等优点，本文介绍的是其中的二次曲线指数平滑法及其在骨坡中，短期预报中的应用。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.