A Neural Network Model for Evaluating Gravel Liquefaction Using Dynamic Penetration Test

2013 ◽  
Vol 275-277 ◽  
pp. 2620-2623
Author(s):  
Qing Xu ◽  
Fei Kang ◽  
Jun Jie Li
Keyword(s):  
Neural Networks ◽  
Earthquake Engineering ◽  
Soil Liquefaction ◽  
Soil Parameters ◽  
Success Rates ◽  
Penetration Test ◽  
Liquefaction Potential ◽  
Gravelly Soil ◽  
Gravelly Soils ◽  
Dynamic Penetration

Evaluation of liquefaction potential of soils is important in geotechnical earthquake engineering. Significant phenomena of gravelly soil liquefaction were reported in 2008 Wenchuan earthquake. Thus, further studies on the liquefaction potential of gravelly soil are needed. This paper investigates the potential of artificial neural networks-based approach to assess the liquefaction potential of gravelly soils form field data of dynamic penetration test. The success rates for occurrence and non-occurrence of liquefaction cases both are 100%. The study suggests that neural networks can successfully model the complex relationship between seismic parameters, soil parameters, and the liquefaction potential of gravelly soils.

Evaluating cyclic liquefaction potential using the cone penetration test

1998 ◽  
Vol 35 (3) ◽  
pp. 442-459 ◽  
Author(s):  
P K Robertson ◽  
CE (Fear) Wride
Keyword(s):  
Earthquake Engineering ◽  
Sandy Soils ◽  
Cone Penetration Test ◽  
Soil Liquefaction ◽  
Penetration Test ◽  
Cone Penetration ◽  
Liquefaction Resistance ◽  
Liquefaction Potential ◽  
Engineering Research ◽  
Research Workshop

Soil liquefaction is a major concern for structures constructed with or on sandy soils. This paper describes the phenomena of soil liquefaction, reviews suitable definitions, and provides an update on methods to evaluate cyclic liquefaction using the cone penetration test (CPT). A method is described to estimate grain characteristics directly from the CPT and to incorporate this into one of the methods for evaluating resistance to cyclic loading. A worked example is also provided, illustrating how the continuous nature of the CPT can provide a good evaluation of cyclic liquefaction potential, on an overall profile basis. This paper forms part of the final submission by the authors to the proceedings of the 1996 National Center for Earthquake Engineering Research workshop on evaluation of liquefaction resistance of soils.Key words: cyclic liquefaction, sandy soils, cone penetration test

scholarly journals Soil Liquefaction Assessment Using Soft Computing Approaches Based on Capacity Energy Concept

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 330
Author(s):  
Zhixiong Chen ◽  
Hongrui Li ◽  
Anthony Teck Chee Goh ◽  
Chongzhi Wu ◽  
Wengang Zhang
Keyword(s):  
Relative Density ◽  
Soft Computing ◽  
Earthquake Engineering ◽  
Textural Properties ◽  
Confining Pressure ◽  
Soil Liquefaction ◽  
Multivariate Adaptive Regression Splines ◽  
Soil Parameters ◽  
Liquefaction Resistance ◽  
Energy Concept

Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The conventional procedures developed to determine the liquefaction potential of sandy soil deposits can be categorized into three main groups: Stress-based, strain-based, and energy-based procedures. The main advantage of the energy-based approach over the remaining two methods is the fact that it considers the effects of strain and stress concurrently unlike the stress or strain-based methods. Several liquefaction evaluation procedures and approaches have been developed relating the capacity energy to the initial soil parameters, such as the relative density, initial effective confining pressure, fine contents, and soil textural properties. In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. The results clearly prove the capability of the proposed models and the capacity energy concept to assess liquefaction resistance of soils. It is also proposed that these approaches should be used as cross-validation against each other. The result shows that the capacity energy is most sensitive to the relative density.

scholarly journals Probabilistic version of the Robertson and Wride method for liquefaction evaluation: development and application

2012 ◽  
Vol 49 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Chih-Sheng Ku ◽  
C. Hsein Juang ◽  
Chi-Wen Chang ◽  
Jianye Ching
Keyword(s):  
Factor Of Safety ◽  
Probabilistic Models ◽  
Deterministic Model ◽  
Soil Liquefaction ◽  
Penetration Test ◽  
Cone Penetration ◽  
Liquefaction Potential ◽  
Additional Information ◽  
Extra Effort ◽  
Probabilistic Version

The Robertson and Wride method is the most widely used cone penetration test (CPT)-based method for soil liquefaction evaluation. This method is a deterministic model, which expresses liquefaction potential in terms of factor of safety. On many occasions, there is a need to express the liquefaction potential in terms of liquefaction probability. Although several probabilistic models are available in the literature, there is an advantage having a probabilistic version of the Robertson and Wride method so that the engineer who prefers to use this method can obtain additional information of liquefaction probability with minimal extra effort. In this paper, a simple model is developed, which links the factor of safety determined by the Robertson and Wride method to the liquefaction probability. The model, referred to as the probabilistic RW model, is developed, and verified, in a mathematically rigorous manner. Simplified equations for assessing the variation of liquefaction probability caused by the uncertainty in input parameters are also developed. Example applications are presented to demonstrate the developed models.

scholarly journals Standard Penetration Test-Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

2004 ◽  
Vol 130 (12) ◽  
pp. 1314-1340 ◽  
Author(s):  
K. Onder Cetin ◽  
Raymond B. Seed ◽  
Armen Der Kiureghian ◽  
Kohji Tokimatsu ◽  
Leslie F. Harder ◽  
...  
Keyword(s):  
Standard Penetration Test ◽  
Soil Liquefaction ◽  
Penetration Test ◽  
Liquefaction Potential

Evaluation of Liquefaction Potential Using Neural Networks Based on Adaptive Resonance Theory

2005 ◽  
Vol 1936 (1) ◽  
pp. 192-200 ◽  
Author(s):  
Pradeep U. Kurup ◽  
Amit Garg
Keyword(s):  
Neural Networks ◽  
Adaptive Resonance Theory ◽  
Fuzzy Artmap ◽  
Multilayer Perceptrons ◽  
Penetration Test ◽  
Resonance Theory ◽  
Liquefaction Potential ◽  
Adaptive Resonance ◽  
Artificial Neural

The incomprehensible loss of lives and extensive damages to transportation facilities caused by earthquakes emphasize the need for robust and reliable methods for evaluating the liquefaction potential of sites. Traditional methods for evaluating liquefaction potential are based on correlating data from the standard penetration test (blow count, N), cone penetration test (cone resistance, qc), or the shear wave velocity ( Vs) with the cyclic stress ratio. These methods are unable to incorporate the complex influence of various soil and in situ state parameters. This problem encouraged the development of numerous nontraditional methods such as artificial neural networks that try to learn and account for the influence of various soil and in situ state properties. The possibility of using neural networks based on adaptive resonance theory (ART) for the prediction of liquefaction potential was explored. These networks have been shown to be far more efficient and reliable than the commonly used backpropagation artificial neural network and other multilayer perceptrons. Two Fuzzy ARTMAP (FAM) models were developed and tested with qc and Vs data obtained from past case histories. The qc-and Vs-based FAM models gave overall successful prediction rates of 98% and 97%, respectively. The promising results obtained by the FAM models exemplify the potential of nontraditional computing methods for evaluating liquefaction potential.

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