2007
DOI: 10.1108/02644400710718547
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Evaluation of liquefaction potential of soil deposits using artificial neural networks

Abstract: PurposeIn the literature, several empirical methods can be found to predict the occurrence of nonlinear soil liquefaction in soil layers. These methods are limited to the seismic conditions and the parameters used in developing the model. This paper seeks to present General Regression Neural Network (GRNN) model that addresses the collective knowledge built in simplified procedure.Design/methodology/approachThe GRNN model incorporates the soil and seismic parameters of the region. It was developed in four phas… Show more

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Cited by 55 publications
(16 citation statements)
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“…Soil and seismic parameters governing the soil liquefaction potential are incorporated into the model development. The MGGP-based formulation of the soil liquefaction condition (LC) is considered to be as follows [39,40]:…”
Section: Problem Iv: Soil Liquefactionmentioning
confidence: 99%
See 1 more Smart Citation
“…Soil and seismic parameters governing the soil liquefaction potential are incorporated into the model development. The MGGP-based formulation of the soil liquefaction condition (LC) is considered to be as follows [39,40]:…”
Section: Problem Iv: Soil Liquefactionmentioning
confidence: 99%
“…One of the most common methods to solve the liquefaction problem uses experimental data to develop empirical models that relate the variables in the system. Modern techniques such as support vector machines, fuzzy systems, and ANNs have been considered to develop liquefaction prediction models [38][39][40]. Unlike the other soft computing tools, applications of GP and its variants to the liquefaction assessment are difficult to be found.…”
Section: Problem Iv: Soil Liquefactionmentioning
confidence: 99%
“…Artificial neural network (ANN), one of the AI approaches, has the capability to mimic the learning abilities of human brain by processing the data. 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.…”
Section: Introductionmentioning
confidence: 99%
“…These in situ data are used to estimate the potential for "triggering" or initiation of seismically induced liquefaction. In the context of the analyses of in situ data, the estimate of liquefaction potential derived from ELMs can be broadly classified as ͑1͒ deterministic ͑Seed and Idriss 1971;Iwasaki et al 1978;Seed et al 1983;Robertson and Campanella 1985;Seed and De Alba 1986;Shibata and Teparaksa 1988;Goh 1994;Stark and Olson 1995;Robertson and Wride 1998;Juang et al 2000Juang et al , 2003Idriss and Boulanger 2006;Pal 2006;Hanna et al 2007;Goh 2007͒ and͑2͒ probabilistic ͑Liao et al 1988;Toprak et al 1999;Juang et al 2002;Goh 2002;Cetin et al 2002Cetin et al , 2004Lee et al 2003;Sonmez 2003;Lai et al 2004;Sonmez and Gokceoglu 2005;Papathanassiou et al 2005;Holzer et al 2006;Moss et al 2006;Juang and Li 2007͒. This paper attempts to improve liquefaction models by ͑1͒ quantitatively comparing the predictive performance of several ELMs; ͑2͒ identifying the threshold needed to apply the probabilistic ELMs; and ͑3͒ developing an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine ͑SVM͒.…”
Section: Introductionmentioning
confidence: 99%