Uncertainty quantification of landslide runout motion considering soil interdependent anisotropy and fabric orientation

Landslides - Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent...     

Movement process,geomorphological changes,and influencing factors of a reactivated loess landslide on the right bank of the middle of the Yellow River,China

Landslides - According to local villagers, the main Beiguo landslide on the Heyang Loess Tableland in China occurred several decades ago (the specific time is unknown). After the initial occurrence...     

Bayesian active learning for parameter calibration of landslide run-out models

Landslides - Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot...     

蒙特卡罗法在地热资源评价中的应用

通过印度尼西亚B地热田地质勘查和已有资料分析,参照中国地热行业勘查和资源评价相关规范,对比该地热田已有的资源评价结果;以热储法（体积法）为基本公式,运用蒙特卡罗（Monte Carlo）法进行预测,随机参数均为地热地质条件限定下的取值范围;最终以体积法估算结果作为评估资源量下限,获得待评价地热田的可发电量资源潜力,为该地热田的进一步开发提供可靠依据。相比传统方法,蒙特卡罗法更适用于地热勘查初级阶段的地热资源评价,具有预测范围合理、数据结果定量化、可提供概率分析置信区间等优点。     

WV-3遥感数据在滇西矿产资源开发与地质灾害调查中的应用

选择云南三江中南段高黎贡山之南作为研究对象,利用WorldView-3卫星数据高空间分辨率的特点,基于Skyline软件构建三维模拟场景,从不同视域范围、远近尺度、方位视角对解译目标进行全方位展示,获取了不同矿山开发及地质灾害的典型识别标志,进行矿产资源开发状况精细识别及地质灾害详细信息提取,解译出区内建筑石料用灰岩等5种非金属矿以及滑坡等3种类型地质灾害,为矿山管理及地质灾害调查提供快速精准的目标,提升工作效率和质量。     

Characterizing uncertainty in geotechnical design of energy piles based on Bayesian theorem

It is known that a lot of uncertainties are involved in geotechnical design of energy piles. In this paper, a Bayesian updating framework is presented to characterize those uncertainties. The load-transfer model is developed to predict the thermomechanical response of energy piles. Considering the cross-case variability of the uncertainty in the axial strains of pile, the global model bias is firstly calibrated by establishing a comprehensive database consisting of 12 energy pile cases. Furthermore, the uncertainty in input parameters is considered in the Bayesian updating of model bias in a specific case. The variability of the uncertain parameters is effectively reduced after updating. The coefficient of variation of prediction is decreased from 0.34 to 0.13. The present framework can well quantify uncertain factors and improve the accuracy and reliability of the prediction model.

     

Developing a novel hybrid model for seismic loss prediction of regional-scale buildings

Bulletin of Earthquake Engineering - Seismic loss prediction of regional-scale buildings provides key information for disaster management. However, previous seismic loss prediction models ignored...     

Estimation of constant-damage residual displacements in terms of maximum inelastic displacements for SDOF structures

Bulletin of Earthquake Engineering - This paper investigates the constant-damage residual displacement ratios Cr (i.e., the ratio of the residual displacement in terms of the maximum inelastic...