Joint exploration of regional importance of possibilistic and probabilistic uncertainty in stability analysis     

《Computers and Geotechnics》2014

Stability analysis generally relies on the estimate of failure probability P. When information is scarce, incomplete, imprecise or vague, this estimate is imprecise. To represent epistemic uncertainty, possibility distributions have shown to be a more flexible tool than probability distributions. The joint propagation of possibilistic and probabilistic information can rely on more advanced techniques such as the classical random sampling of the cumulative probability distribution F and of the intervals from the possibility distributions π. The imprecise probability P is then associated with a random interval, which can be summarized by a pair of indicators bounding it. In the present paper, we propose a graphical tool to explore the sensitivity on these indicators. This is conducted by means of the contribution to sample probability of failure plot based on the ordering of the randomly generated levels of confidence associated with the quantiles of F and to the α-cuts of π. This presents several advantages: (1) the contribution of both types of uncertainty, aleatoric and epistemic, can be compared in a unique setting; (2) the analysis is conducted in a post-processing step, i.e. at no extra computational cost; (3) it allows highlighting the regions of the quantiles and of the nested intervals which contribute the most to the bounds of P. The method is applied on two case studies (a mine pillar and a steep slope stability analysis) to investigate the necessity for extra data acquisition on parameters whose imprecision can hardly be modelled by probabilities due to the scarcity of the available information (respectively the extraction ratio and the cliff geometry).  相似文献   

Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns     

《Computers and Geotechnics》2014

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.  相似文献   

辽宁参考作物腾发量变化特征及成因分析     

温日红  肇同斌  张钰祺  冯艾琳  谢艳兵  姜鹏  王一  崔雪  王笑影 《沙漠与绿洲气象(新疆气象)》2024,18(4):158-165

利用1961-2020年辽宁地区62个国家级气象观测站生长季的逐日气象观测数据,采用Penman-Monteith公式计算了辽宁地区参考作物腾发量（ET0）,利用ArcGIS的克里格插值法、M-K检验分析了辽宁地区生长季ET0的时空分布特征,对影响ET0变化的成因进行了分析。结果表明,近60年辽宁地区生长季参考作物腾发量呈现由西北向东南递减的变化趋势;ET0在1961-2010年呈下降趋势,2011-2020年呈升高趋势,生长季多年平均ET0变化趋势表现为波动下降趋势;生长季内ET0对相对湿度的响应最为敏感,为负效应。ET0对风速和温度变化的响应敏感性相对较小,为正效应。湿度的敏感系数绝对值明显高于风速和温度,7月份达到峰值;多年相对变化率绝对值最大的是风速,其次是温度和相对湿度;三个气象要素对ET0贡献最大的是风速,温度和湿度对ET0的正贡献不及风速的负贡献,综合敏感性和贡献两方面因素分析,风速的变化趋势为ET0呈下降趋势的主导因子。  相似文献   

边界层过程对暴雨影响的敏感性试验   总被引：12，自引：11，他引：1       下载免费PDF全文

赵鸣  陈潜 《气象科学》2007,27(1):1-10

用MM5v3模式对江淮暴雨进行了计入与不计入边界层和计入与不计入地面通量的敏感性试验,结果指出二者的影响是一致的,地面通量是边界层影响的主要部分。一般说,不计边界层影响,则湿位涡、水汽通量散度、涡度、低空急流等分布发生变化,其中心位置改变,强度也有改变(大部分减弱),造成总的降水减弱和降水位置的改变。边界层正是通过湿位涡、水汽通量散度、涡度、低空急流等因素的综合影响来影响暴雨,并影响暴雨的落区和强度。  相似文献   

遥感反演中参数的不确定性与敏感性矩阵   总被引：26，自引：6，他引：20  

李小文  高峰  王锦地  朱启疆 《遥感学报》1997,1(1):5-14

多角度遥感中二向性反射（ＢＲＤＦ）物理模型的反演,是当前国际遥感界学术研究的热点之一,反演中先验知识的表达和利用,参数敏感性的判别和处理,已提出了若干年,但只是到最近,才有了将参数的先验概率引入拟合误差平方加权,及定义参数对观测值敏感性的尝试,该文简述了这些最新进展,并首次建议了反演中参数的“不确定性与敏感性矩阵”的定义,并以作者最近的反演实例,说明这样定义的矩阵,如何直接用于反演的多阶段目标决策  相似文献