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Validation and application of empirical liquefaction models / Thomas Oommen in Journal of geotechnical and geoenvironmental engineering, Vol. 136 N° 12 (Décembre 2010) 

Public ISBD [article]  in Journal of geotechnical and geoenvironmental engineering > Vol. 136 N° 12 (Décembre 2010) . - pp. 1618-1633 Titre : Validation and application of empirical liquefaction models Type de document : texte imprimé Auteurs : Thomas Oommen, Auteur ; Laurie G. Baise, Auteur ; Richard Vogel, Auteur Année de publication : 2011 Article en page(s) : pp. 1618-1633 Note générale : Géotechnique Langues : Anglais (eng) Mots-clés : Bayesian  updating  method  Support  vector  machine  Simplified  procedure  Model  validation  CPT  SPT  Machine  learning Index. décimale : 624.1 Infrastructures.Ouvrages en terre. Fondations. Tunnels Résumé : Empirical liquefaction models (ELMs) are the standard approach for predicting the occurrence of soil liquefaction. These models are typically based on in situ index tests, such as the standard penetration test (SPT) and cone penetration test (CPT), and are broadly classified as deterministic and probabilistic models. No objective and quantitative comparison of these models have been published. Similarly, no rigorous procedure has been published for choosing the threshold required for probabilistic models. This paper provides (1) a quantitative comparison of the predictive performance of ELMs; (2) a reproducible method for choosing the threshold that is needed to apply the probabilistic ELMs; and (3) an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine (SVM). Deterministic and probabilistic ELMs have been developed for SPT and CPT data. For deterministic ELMs, we compare the “simplified procedure,” the Bayesian updating method, and the SVM models for both SPT and CPT data. For probabilistic ELMs, we compare the Bayesian updating method with the SVM models. We compare these different approaches within a quantitative validation framework. This framework includes validation metrics developed within the statistics and artificial intelligence fields that are not common in the geotechnical literature. We incorporate estimated costs associated with risk as well as with risk mitigation. We conclude that (1) the best performing ELM depends on the associated costs; (2) the unique costs associated with an individual project directly determine the optimal threshold for the probabilistic ELMs; and (3) the more recent ELMs only marginally improve prediction accuracy; thus, efforts should focus on improving data collection. DEWEY : 624.1 ISSN : 1090-0241 En ligne : http://ascelibrary.org/gto/resource/1/jggefk/v136/i12/p1618_s1?isAuthorized=no [article] Validation and application of empirical liquefaction models [texte imprimé] / Thomas Oommen, Auteur ; Laurie G. Baise, Auteur ; Richard Vogel, Auteur . - 2011 . - pp. 1618-1633. Géotechnique Langues : Anglais (eng) in Journal of geotechnical and geoenvironmental engineering > Vol. 136 N° 12 (Décembre 2010) . - pp. 1618-1633 Mots-clés : Bayesian  updating  method  Support  vector  machine  Simplified  procedure  Model  validation  CPT  SPT  Machine  learning Index. décimale : 624.1 Infrastructures.Ouvrages en terre. Fondations. Tunnels Résumé : Empirical liquefaction models (ELMs) are the standard approach for predicting the occurrence of soil liquefaction. These models are typically based on in situ index tests, such as the standard penetration test (SPT) and cone penetration test (CPT), and are broadly classified as deterministic and probabilistic models. No objective and quantitative comparison of these models have been published. Similarly, no rigorous procedure has been published for choosing the threshold required for probabilistic models. This paper provides (1) a quantitative comparison of the predictive performance of ELMs; (2) a reproducible method for choosing the threshold that is needed to apply the probabilistic ELMs; and (3) an alternative deterministic and probabilistic ELM based on the machine learning algorithm, known as support vector machine (SVM). Deterministic and probabilistic ELMs have been developed for SPT and CPT data. For deterministic ELMs, we compare the “simplified procedure,” the Bayesian updating method, and the SVM models for both SPT and CPT data. For probabilistic ELMs, we compare the Bayesian updating method with the SVM models. We compare these different approaches within a quantitative validation framework. This framework includes validation metrics developed within the statistics and artificial intelligence fields that are not common in the geotechnical literature. We incorporate estimated costs associated with risk as well as with risk mitigation. We conclude that (1) the best performing ELM depends on the associated costs; (2) the unique costs associated with an individual project directly determine the optimal threshold for the probabilistic ELMs; and (3) the more recent ELMs only marginally improve prediction accuracy; thus, efforts should focus on improving data collection. DEWEY : 624.1 ISSN : 1090-0241 En ligne : http://ascelibrary.org/gto/resource/1/jggefk/v136/i12/p1618_s1?isAuthorized=no