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Support vector machines approach to HMA stiffness prediction / Kasthurirangan Gopalakrishnan in Journal of engineering mechanics, Vol. 137 N° 2 (Fevrier 2011) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 137 N° 2 (Fevrier 2011) . - pp.138-146 Titre : Support vector machines approach to HMA stiffness prediction Type de document : texte imprimé Auteurs : Kasthurirangan Gopalakrishnan, Auteur ; Sunghwan Kim, Auteur Année de publication : 2011 Article en page(s) : pp.138-146 Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Support  vector  machines  (SVM)  Asphalt  Dynamic  modulus  Artificial  intelligence  (AI)  Construction  materials. Résumé : The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E∗|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E∗| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E∗| prediction models were developed using the latest comprehensive |E∗| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E∗| model as well as the artificial neural networks (ANN) based |E∗| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v137/i2/p138_s1?isAuthorized=no [article] Support vector machines approach to HMA stiffness prediction [texte imprimé] / Kasthurirangan Gopalakrishnan, Auteur ; Sunghwan Kim, Auteur . - 2011 . - pp.138-146. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 137 N° 2 (Fevrier 2011) . - pp.138-146 Mots-clés : Support  vector  machines  (SVM)  Asphalt  Dynamic  modulus  Artificial  intelligence  (AI)  Construction  materials. Résumé : The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E∗|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E∗| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E∗| prediction models were developed using the latest comprehensive |E∗| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E∗| model as well as the artificial neural networks (ANN) based |E∗| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v137/i2/p138_s1?isAuthorized=no