Behavior of a fine-grained soil during the Loma Prieta earthquake: Reply

1999 ◽  
Vol 36 (3) ◽  
pp. 584 ◽  
Author(s):  
Ross W Boulanger ◽  
Lelio H Mejia ◽  
Mark W Myers ◽  
Izzat M Idriss
Keyword(s):  
Loma Prieta Earthquake ◽  
Fine Grained ◽  
Fine Grained Soil ◽  
Loma Prieta

Behavior of a fine-grained soil during the Loma Prieta earthquake

1998 ◽  
Vol 35 (1) ◽  
pp. 146-158 ◽  
Author(s):  
Ross W Boulanger ◽  
Mark W Meyers ◽  
Lelio H Mejia ◽  
Izzat M Idriss
Keyword(s):  
Clayey Soil ◽  
Laboratory Data ◽  
Lateral Spreading ◽  
Liquid Limit ◽  
Silty Clay ◽  
Loma Prieta Earthquake ◽  
Fine Grained ◽  
Fine Grained Soil ◽  
Loma Prieta ◽  
Excess Pore Pressures

Results of an investigation into the behavior of a fine-grained clayey soil at Moss Landing during the 1989 Loma Prieta earthquake are presented. A deposit of this soil underlies portions of the Moss Landing Marine Laboratory that experienced up to 1.3 m of lateral spreading deformations during this magnitude 7 earthquake. Silty clay from the deposit erupted to the surface in a "soil boil" characteristic of liquefaction, during and immediately after the earthquake. A sample from the silty clay boil had a liquid limit of 38, a plasticity index of 17, and a <5 µm fraction of 24%, and thus would be considered nonliquefiable according to commonly used criteria. Analysis of cyclic triaxial test data suggests that portions of the silty clay deposit likely developed high residual excess pore pressures (ru,r approx 80-90%) and significant shear strains during the earthquake and thus likely contributed to the observed lateral deformations. The field and laboratory data show that commonly used criteria for identifying "liquefiable" clayey soils should be applied with caution and should not be indiscriminately viewed as a substitute for detailed laboratory and in situ testing of low plasticity fine-grained soils.Key words: liquefaction, cyclic loading, silt, clay, earthquake, case history.

scholarly journals Modeling the Compaction Characteristics of Fine-Grained Soils Blended with Tire-Derived Aggregates

2021 ◽  
Vol 13 (14) ◽  
pp. 7737
Author(s):  
Amin Soltani ◽  
Mahdieh Azimi ◽  
Brendan C. O’Kelly
Keyword(s):  
Energy Levels ◽  
Model Development ◽  
Dry Mass ◽  
Unit Weight ◽  
Power Functions ◽  
Compaction Characteristics ◽  
Practical Applications ◽  
Fine Grained ◽  
Practical Procedure ◽  
Fine Grained Soil

This study aims at modeling the compaction characteristics of fine-grained soils blended with sand-sized (0.075–4.75 mm) recycled tire-derived aggregates (TDAs). Model development and calibration were performed using a large and diverse database of 100 soil–TDA compaction tests (with the TDA-to-soil dry mass ratio ≤ 30%) assembled from the literature. Following a comprehensive statistical analysis, it is demonstrated that the optimum moisture content (OMC) and maximum dry unit weight (MDUW) for soil–TDA blends (across different soil types, TDA particle sizes and compaction energy levels) can be expressed as universal power functions of the OMC and MDUW of the unamended soil, along with the soil to soil–TDA specific gravity ratio. Employing the Bland–Altman analysis, the 95% upper and lower (water content) agreement limits between the predicted and measured OMC values were, respectively, obtained as +1.09% and −1.23%, both of which can be considered negligible for practical applications. For the MDUW predictions, these limits were calculated as +0.67 and −0.71 kN/m3, which (like the OMC) can be deemed acceptable for prediction purposes. Having established the OMC and MDUW of the unamended fine-grained soil, the empirical models proposed in this study offer a practical procedure towards predicting the compaction characteristics of the soil–TDA blends without the hurdles of performing separate laboratory compaction tests, and thus can be employed in practice for preliminary design assessments and/or soil–TDA optimization studies.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

Physical and Mechanical Properties of Fine-Grained Soil in the Zhejiang-Fujian Coastal Area, China

2011 ◽  
Vol 29 (4) ◽  
pp. 333-345 ◽  
Author(s):  
Yuan-Qin Xu ◽  
Pei-Ying Li ◽  
Ping Li ◽  
Le-Jun Liu ◽  
Cheng-Xiao Cao ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Coastal Area ◽  
Physical And Mechanical Properties ◽  
Fine Grained ◽  
Fine Grained Soil

scholarly journals Centrifuge testing of soil–structure interaction effects on cyclic failure potential of fine-grained soil

2021 ◽  
pp. 875529302098197
Author(s):  
Jason M Buenker ◽  
Scott J Brandenberg ◽  
Jonathan P Stewart
Keyword(s):  
Soil Structure ◽  
Interaction Effects ◽  
Soil Structure Interaction ◽  
Centrifuge Testing ◽  
Geotechnical Centrifuge ◽  
Structure Interaction ◽  
Fine Grained ◽  
Stiffness Properties ◽  
Fine Grained Soil ◽  
Strip Footings

We describe two experiments performed on a 9-m-radius geotechnical centrifuge to evaluate dynamic soil–structure interaction effects on the cyclic failure potential of fine-grained soil. Each experiment incorporated three different structures with a range of mass and stiffness properties. Structures were founded on strip footings embedded in a thin layer of sand overlying lightly overconsolidated low-plasticity fine-grained soil. Shaking was applied to the base of the model container, consisting of scaled versions of recorded earthquake ground motions, sweep motions, and step waves. Data recorded during testing were processed and published on the platform DesignSafe. We describe the model configuration, sensor information, shaking events, and data processing procedures and present selected processed data to illustrate key model responses and to provide a benchmark for data use.

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