Assessment of liquefaction potential using neural networks

Tung, Albert; Wang, Ya Yung; Wong, Fiona

doi:10.1016/0267-7261(93)90035-p

Cited by 16 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN techniques have also been used to model the liquefaction potential (e.g. Baziar and Nilipour 2003;Goh 1994Goh , 1996Goh , 2002Tung et al 1993). …”

Section: Application Of Soft Computing For Liquefactionmentioning

confidence: 99%

Seismic risk analysis using Bayesian belief networks

Tesfamariam

Liu

2013

Handbook of Seismic Risk Analysis and Management of Civil Infrastructure Systems

View full text Add to dashboard Cite

“…ANN techniques have also been used to model the liquefaction potential (e.g. Baziar and Nilipour 2003;Goh 1994Goh , 1996Goh , 2002Tung et al 1993). …”

Section: Application Of Soft Computing For Liquefactionmentioning

confidence: 99%

Seismic risk analysis using Bayesian belief networks

Tesfamariam

Liu

2013

Handbook of Seismic Risk Analysis and Management of Civil Infrastructure Systems

View full text Add to dashboard Cite

“…Because triggering refers to liquefaction at discrete depths in a profile, the outputs from triggering analysis are often used in series with manifestation models to predict a profile's system response in the form of settlement, ejecta, spreading, cracking, and so on (e.g. Hutabarat and Bray, 2022; Tung et al (1993) Assessment of liquefaction potential using neural networks Goh (1996) Neural-network modeling of CPT seismic liquefaction data Hsein Juang et al (1999) Appraising cone penetration test-based liquefaction resistance evaluation methods: artificial neural network approach Juang and Chen (1999) CPT-based liquefaction evaluation using artificial neural networks Wang and Rahman (1999) A neural network model for liquefaction-induced horizontal ground displacement Chiru-Danzer et al (2001) Estimation of liquefaction-induced horizontal displacements using artificial neural networks Rahman and Wang (2002) Fuzzy neural network models for liquefaction prediction Baziar and Nilipour (2003) Evaluation of liquefaction potential using neuralnetworks and CPT results Hao et al (2004) Evaluation of sands liquefaction potential based on SOFM neural network Baziar and Ghorbani (2005) Evaluation of lateral spreading using artificial neural networks Garg (2005) Evaluation of liquefaction potential using adaptive resonance theory based neural networks Kurup and Garg (2005) Evaluation of liquefaction potential using neural networks based on adaptive resonance theory Javadi et al (2006) Evaluation of liquefaction induced lateral displacements using genetic programming Liu et al (2006) Artificial neural network methodology for soil liquefaction evaluation using CPT values Pal (2006) Support vector machines-based modelling of seismic liquefaction potential Goh and Goh (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data Hanna et al (2007) Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data Khozaghi and Choobbasti (2007) Predicting of liquefaction potential in soils using artificial neural networks Chen et al (2008) Empirical model for liquefaction resistance of soils based on artificial neural network learning of case histories Chern et al (2008) CPT-based liquefaction assessment by using fuzzyneural network Garcia et al (2008) A neurofuzzy system to analyze liquefaction-induced lateral spread…”

Section: Literature Overview and Backgroundmentioning

confidence: 99%

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Maurer

Sanger

2023

Earthquake Spectra

View full text Add to dashboard Cite

Soil liquefaction remains an important and interesting problem that has attracted the development of enumerable prediction models. Increasingly, these models are utilizing algorithmic learning or “artificial intelligence” (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this article is to investigate “why?” Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state-of-practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Toward this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near-term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena.

show abstract

“… used probabilistic and deterministic methods for determination of seismic liquefaction potential based on SPT. Artificial neural network (ANN) has been successfully applied for prediction of liquefaction susceptibility of soil .…”

Section: Introductionmentioning

confidence: 99%

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

Samui

Karthikeyan

2012

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

SUMMARYThis article adopts least square support vector machine (LSSVM) for determination of liquefactions susceptibility of soil based on standard penetration test data. Two models (Models I and II) have been developed. For Model I, input variables are cyclic stress ratio and standard penetration test value (N). Model II employs peak ground acceleration and N as input variables. The developed LSSVM models (Models I and II) give equations for determination of liquefaction susceptibility of soil. The performances of Models I and II are the same. The developed LSSVM gives probabilistic output. The results of LSSVM have been compared with the artificial neural network model. This article shows that N and the peak ground acceleration are sufficient input parameters for determination of liquefaction susceptibility of soil.

show abstract

Assessment of liquefaction potential using neural networks

Cited by 16 publications

References 2 publications

Seismic risk analysis using Bayesian belief networks

Seismic risk analysis using Bayesian belief networks

Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn’t be

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

Contact Info

Product

Resources

About