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【讲座预告0420】岩土力学参数的统计特征研究

Created Date 4/17/2018    View Numbers  536 Return

报告题目:Estimation of soil properties using statistical learning(岩土力学参数的统计特征研究)

报 告 人: 方国光(Kok-Kwang Phoon)教授(新加坡工程院院士、新加坡国立大学副校长) 

会议时间:2018年4月20日(星期五)下午15:00-16:30

活动地点:校本部J101

 

报告简介 

   ABSTRACT:Site investigation is required for all geotechnical projects, because every site is unique to some extent. One key purpose of site investigation is to characterize soil/rock properties for design using a transformation model. One typical example is undrained shear strength (su) in kPa = 6´SPT blow count (N). An experienced engineer understood that the factor 6 is site dependent. It is also well understood that this transformation model is very simple “rule-of-thumb” and there are significant uncertainties associated with this property estimation process. That is, the actual value of sucan be higher or lower than 6N. The latter possibility is unconservative and can result in costly remediation. The engineer tries to mitigate this by using a “cautious” estimate (called a characteristic value), but this approach relies more on engineering judgment than the actual data at hand. To have a sense of the probability of (su< 6N), one needs to quantify the transformation uncertainty explicitly.

    In summary, a realistic statistical approach to handle geotechnical data will need to consider: (1) multivariate data (e.g. su, N), (2) transformation uncertainty that is possibly unique to each site, and (3) statistical uncertainty that is related to the number of measurements. There is a further “incompleteness” complication – it is common to measure N without suat a given depth. Hence, if the multivariate data were to be stored in a table, there are empty cells in the table. In short, the characteristics of geotechnical data, which can vary in space and time, can be succinctly described as MUSIC – Multivariate, Uncertain and unique, Sparse, and Incomplete. This lecture presents the latest research in applying statistical learning to estimation of soil properties.

报告人简介

 

       方国光教授,1988和1990分别获得新加坡国立大学学士和硕士学位,1995年获美国康奈尔大学博士学位,现担任国际土力学与岩土工程学会(ISSMGE)下属TC304(风险评估与管理)技术委员会主席、新加坡工程院院士、美国土木工程师协会会士(ASCE Fellow)、新加坡国立大学杰出教授(Distinguished Professor)、副校长(Vice Provost)、国家千人计划专家、国家杰出青年基金B类获得者,曾担任新加坡岩土工程学会主席。

       方国光教授主要从事岩土工程可靠度分析与风险控制、岩土工程随机模拟方法以及大规模岩土工程问题的数值方法方面的研究。主持了中国国家自然科学基金委员会海外及港澳学者合作研究基金(原杰青B类)以及10余项新加坡政府研究基金和其它政府部门基金资助项目。在国际权威学术期刊发表论文300余篇,其中150余篇被SCI 检索,SCI 论文他引2000余次,单篇文章最高被引用480次,Web of Science 数据库H指数为30。主编及参编了16 本专著和论文集,参编3 项美国土木工程师学会(ASCE)标准。主办及协办了44 次国际学术会议,其中18 次担任国际会议主席或副主席,应邀做过33 次大会或特邀报告。主要国际学术荣誉有2005年获ASCE诺曼奖章(Norman Medal)、2007年获美国材料试验学会(ASTM)的Hogentogler奖、2008年和2014年分别荣获国际岩土力学计算方法与进展协会(IACMAG)地区杰出贡献奖和“约翰·布克奖章”(John Booker Medal)。担任国际期刊Georisk 创刊主编及11 个岩土工程领域国际主流学术期刊编委。在16 个国际学术组织担任主席或核心成员。


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