Rockhead profile simulation using an improved generation method of conditional random field  

作　　者：Liang Han Lin Wang Wengang Zhang Boming Geng Shang Li 

机构地区：[1]School of Civil Engineering,Chongqing University,Chongqing,400044,China [2]Key Laboratory of New Technology for Construction of Cities in Mountain Area,Chongqing University,Chongqing,400044,China [3]National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas,Chongqing University,Chongqing,400044,China [4]China Railway 19th Bureau Group Sixth Engineering Co.,Ltd.,Wuxi,214028,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2022年第3期896-908,共13页岩石力学与岩土工程学报（英文版）

基　　金：the funding support from the National Natural Science Foundation of China (Grant No. 52078086);Program of Distinguished Young Scholars, Natural Science Foundation of Chongqing, China (Grant No. cstc2020jcyj-jq0087);State Education Ministry and the Fundamental Research Funds for the Central Universities (Grant No. 2019 CDJSK 04 XK23)

摘　　要：Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice,and thus,it is necessary to establish a useful method to reverse the rockhead profile using site investigation results.As a general method to reflect the spatial distribution of geo-material properties based on field measurements,the conditional random field(CRF)was improved in this paper to simulate rockhead profiles.Besides,in geotechnical engineering practice,measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent.As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty,CRF was implemented with the aid of the Bayesian framework in this study.More importantly,this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work.The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result,the subjectivity in determining prior mean can be minimized.Finally,both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles,while the influence of the latter is less significant than that of the former.

关 键 词：Rockhead profile BOREHOLE Conditional random field(CRF) BAYESIAN Mean uncertainty 

分 类 号：TU45[建筑科学—岩土工程]