摘要
目前考虑土体参数空间变异性的边坡可靠度分析所研究的边坡几何尺寸相对较小。为有效地分析考虑参数空间变异性的几何尺寸相对较大的边坡可靠度问题,提出了基于多重响应面与子集模拟的边坡可靠度分析方法。以一个坡高为24 m的两层非均质黏土边坡为例验证了提出方法的有效性,并探讨了正态、对数正态、极值I型、Gamma和Beta这5种概率分布类型对边坡可靠度的影响。结果表明,提出方法具有以下优势:1可以有效地计算考虑多参数空间变异性的低概率水平边坡可靠度;2可以较好地解决几何尺寸相对较大的边坡可靠度问题;3有较高的参数敏感性分析计算效率,可为调查概率分布类型和波动范围等参数统计特征对边坡可靠度的影响提供技术支持。此外,参数概率分布类型对边坡可靠度具有重要的影响,常用的正态和对数正态分布分别用于表征参数概率分布特征时,可能会分别高估和低估边坡失效概率。
The existing geometries of the slopes in slope reliability analysis considering spatial variability of soil properties are relatively small. An efficient approach based on the multiple response-surface and subset simulation is proposed for solving slope reliability problems involving relatively large slope geometries. An example of reliability analysis of two-layered heterogeneous clay slope with the height of 24 m is presented to demonstrate the effectiveness of the proposed method. The effect of marginal probability distributions, namely Gaussian, lognormal, Extvalue I, Gamma and Beta on slope reliability is investigated. The results indicate that the proposed approach possesses the following advantages：（1） it can properly evaluate the slope reliability at low-probability levels（i.e., 10-9 ~ 10-4） in spatially variable soils;（2） it effectively solves slope reliability problems involving relatively large slope geometries;（3） it greatly improves the computational efficiency in parametric sensitivity analysis, and provides an effective way to investigate the effects of statistics（e.g., probability distribution, scale of fluctuation） on the slope reliability. Additionally, the marginal probability distributions of soil properties significantly affect the slope reliability. The commonly-used Gaussian and lognormal distributions may overestimate and underestimate the probability of slope failure, respectively.
出处
《岩土工程学报》
EI
CAS
CSCD
北大核心
2016年第6期1071-1080,共10页
Chinese Journal of Geotechnical Engineering
基金
长江科学院开放研究基金项目(CKWV2015222/KY)
国家自然科学基金项目(51509125
51409139)
关键词
边坡可靠度
空间变异性
低概率水平
概率分布
子集模拟
slope reliability
spatial variability
low-probability level
probability distribution
subset simulation
