Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy

Baziar, Mohammad Hassan; Jafarian, Yaser

doi:10.1016/j.soildyn.2007.03.007

Cited by 122 publications

(83 citation statements)

References 31 publications

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“…Then, the neural network method was used in many research subjects in engineering geology and civil engineering. The neural network has been applied in structural damage inspection [8], soft rocks strength prediction [9], ground vibration prediction [10], ground surface settlement [11,12], engineering cost prediction [13,14], concrete expansion prediction [15], soil swell potential prediction [16], triaxial compression behavior prediction [17], saturated hydraulic conductivity prediction [18], and other engineering subjects [19][20][21]. Only a fraction of the studies were about predicting the frostheaving behavior of saline soil in Western Jilin [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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Section: Introductionmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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“…Numerous researchers have investigated the liquefaction potential of sands using laboratory methods (e.g., DeAlba et al, 1976;Ladd et al, 1989), field methods (e.g., Davis and Berrill, 2001;Cetin et al, 2004), and numerical techniques (e.g., Chen et al, 2005;Baziar and Jafarian, 2007;Zhang et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Liquefaction Using the Energy Approach

Kayabalı

Yilmaz²,

Fener

et al. 2017

Bulletin of the Mineral Research and Exploration

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Damage to structures during earthquakes may be fully or partly caused by soil liquefaction, which has been the subject of extensive research for several decades. Liquefaction susceptibility of a sandy deposit is performed by comparing the resistance of a soil to liquefaction (i.e., capacity) to the load imparted by an earthquake (i.e., demand). In this regard, the stress-based method of liquefaction assessment is by far the most popular. It involves uncertainties mostly related to the computation of the maximum horizontal ground acceleration (amax) at bedrock. A site response analysis or a simplified assumption is necessary to determine the amax on the ground level as well. Developing from the stressbased approach, the strain-based approach has also similar constraints. There exist laboratory techniques such as torsional shear to determine the capacity of a sandy soil in terms of liquefaction energy per unit volume. Likewise, the energy of a strong motion record can be set by employing simple physics principles.

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“…However, Ishihara and Yasuda (1975) said that the equivalent shear stress is 57% for 20 cycles of loading. Although the procedure has been corrected and augmented periodically since then with the studies by Seed (1979), Seed and Idriss (1982) and Seed et al (1985), the uncertainty concerning random loading still exists (Baziar & Jafarian, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…ANN models were recently adopted by researchers in the area of geotechnical engineering (e.g., Abu Keifa, 1998;Baziar & Jafarian, 2007;Chen, Hsieh, Chen, & Shih, 2005;Goh, 1994Goh, , 1995Hanna, Morcous, & Helmy, 2004, 2007a, Hanna, Ural, & Saygili, 2007bTeh, Wong, Goh, & Jaritngam, 1997;Wang & Rahman, 2002). Such as, Baziar and Jafarian (2007) developed an ANN model to correlate some of the soil parameters with the strain energy required liquefaction triggering. They carried out parametric study and confirmed the results using previously published cyclic triaxial, simple shear and torsional shear test results.…”

Section: Introductionmentioning

confidence: 99%