Evaluation of Data Analysis Methods for the CRS Consolidation Test

被引:14
|
作者
Fox, Patrick J. [1 ]
Pu, He-Fu [1 ]
Christian, John T.
机构
[1] Univ Calif San Diego, Dept Struct Engn, La Jolla, CA 92093 USA
基金
美国国家科学基金会;
关键词
Consolidation test; Constant rate of strain; Nonlinear analysis; Numerical modeling; Clay; LARGE-STRAIN CONSOLIDATION; CONSTANT RATE; MODEL; CLAY; CS2;
D O I
10.1061/(ASCE)GT.1943-5606.0001103
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The constant rate-of-strain (CRS) laboratory consolidation test is used to measure consolidation properties of fine-grained soils. Although the CRS test offers many advantages over the conventional incremental-loading consolidation test, uncertainties associated with the method of data analysis have presented an obstacle to more widespread use of the CRS test in practice. This paper presents results from a numerical investigation of the accuracy of linear and nonlinear data analysis methods for the CRS consolidation test. Numerical simulations were conducted using a validated large strain consolidation model for CRS loading conditions, published material properties for two reconstituted clay soils, and three applied strain rates. Based on the numerical results, recommendations are provided for analysis of CRS consolidation data, including changes to the ASTM D4186 equations for nonlinear data analysis. The most appropriate analysis method for soils with linear compressibility is the current ASTM D4186 linear theory method. The most appropriate analysis method for soils with nonlinear compressibility is the proposed modified nonlinear (MNL) theory method. These recommended congruent analysis methods provided good to excellent results for normally consolidated, steady-state conditions, including constant or variable coefficient of consolidation, but yielded large errors for overconsolidated conditions near the preconsolidation stress. As a precaution, CRS tests for overconsolidated soils should generally be conducted using lower strain rates. (C) 2014 American Society of Civil Engineers.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Interpretation of coefficient of consolidation from CRS test results
    Jia, Rui
    Chai, Jinchun
    Hino, Takenori
    GEOMECHANICS AND ENGINEERING, 2013, 5 (01) : 57 - 70
  • [2] Influence of initial state in CRS consolidation test for Pleistocene clays
    Watabe, Yoichi
    Hatakeyama, Masanori
    Hashizume, Hideo
    Egawa, Yusuke
    NEW ADVANCES IN GEOTECHNICAL ENGINEERING, 2018, : 374 - 379
  • [3] Investigation on the Characteristics of Pore Water Flow During CRS Consolidation Test
    Ahmadi H.
    Rahimi H.
    Soroush A.
    Geotechnical and Geological Engineering, 2011, 29 (6) : 989 - 997
  • [4] EFFECT OF STRAIN DISTRIBUTION PATTERN ON INTERPRETING CRS CONSOLIDATION TEST RESULTS
    Jia, R.
    Chai, J-C.
    RECENT DEVELOPMENTS OF GEOTECHNICAL ENGINEERING, 2010, : 29 - 36
  • [5] Numerical Simulation to Select Proper Strain Rates during CRS Consolidation Test
    Henniche, Abderrahmane
    Belkacemi, Smain
    PERIODICA POLYTECHNICA-CIVIL ENGINEERING, 2018, 62 (02): : 404 - 412
  • [6] Experimental verification of CRS consolidation theory
    Dept of Civ + Envir Engrg, Northeastern Univ, Boston, MA 02115, United States
    J Geotech Geoenvir Eng, 5 (430-437):
  • [7] Experimental verification of CRS consolidation theory
    Sheahan, TC
    Watters, PJ
    JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING, 1997, 123 (05) : 430 - 437
  • [8] Impacts of Unbalanced Test Data on the Evaluation of Classification Methods
    Manh Hung Nguyen
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (03) : 497 - 502
  • [9] Study on Consolidation Coefficient by Different Test Methods
    Tang Yanchun
    Meng Gaotou
    Gong Jichang
    ADVANCED DESIGN TECHNOLOGY, PTS 1-3, 2011, 308-310 : 1778 - +
  • [10] Validation of parameters for the consolidation test at constant strain rate "CRS" for soils of Cochabamba-Bolivia
    Antezana Lopez, Franz Pablo
    Cruz Chuquichambi, Abel
    GEOTECHNICAL ENGINEERING IN THE XXI CENTURY: LESSONS LEARNED AND FUTURE CHALLENGES, 2019, : 13 - 21