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Intelligent damage detection in bridge girders / Ahmed H. Al-Rahmani in Journal of engineering mechanics, Vol. 139 N° 3 (Mars 2013) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 139 N° 3 (Mars 2013) . - pp.296–304. Titre : Intelligent damage detection in bridge girders : hybrid approach Type de document : texte imprimé Auteurs : Ahmed H. Al-Rahmani, Auteur ; Hayder A. Rasheed, Auteur ; Yacoub Najjar, Auteur Année de publication : 2013 Article en page(s) : pp.296–304. Note générale : Applied mechanics Langues : Anglais (eng) Mots-clés : Damage  detection  Finite-element  analysis  Artificial  neural  network Résumé : This study is intended to facilitate damage detection in concrete bridge girders without the need for visual inspection while minimizing field measurements. Beams with different material and cracking parameters were modeled using ABAQUS finite-element analysis software to obtain stiffness values at specified nodes. The resulting database was then used to train an artificial neural network (ANN) model to inversely predict the most probable cracking pattern. The aim is to use the ANN approach to solve an inverse problem where a unique analytical solution is not attainable. Accordingly, simple span beams with three, five, seven, and nine stiffness nodes and a single crack were modeled in this work. To confirm that the ANN approach can characterize the logic within the databases, networks with geometric, material, and cracking parameters as inputs and stiffness values as outputs were created. These networks provided excellent prediction accuracy measures ([Math Processing Error]). For the inverse problem, the noted trend shows that better prediction accuracy measures are achieved when more stiffness nodes are used in the ANN modeling process. It was also observed that providing some outputs to the ANN as inputs, thus decreasing the number of required outputs, immensely improves the quality of predictions provided by the ANN. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EM.1943-7889.0000536 [article] Intelligent damage detection in bridge girders : hybrid approach [texte imprimé] / Ahmed H. Al-Rahmani, Auteur ; Hayder A. Rasheed, Auteur ; Yacoub Najjar, Auteur . - 2013 . - pp.296–304. Applied mechanics Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 139 N° 3 (Mars 2013) . - pp.296–304. Mots-clés : Damage  detection  Finite-element  analysis  Artificial  neural  network Résumé : This study is intended to facilitate damage detection in concrete bridge girders without the need for visual inspection while minimizing field measurements. Beams with different material and cracking parameters were modeled using ABAQUS finite-element analysis software to obtain stiffness values at specified nodes. The resulting database was then used to train an artificial neural network (ANN) model to inversely predict the most probable cracking pattern. The aim is to use the ANN approach to solve an inverse problem where a unique analytical solution is not attainable. Accordingly, simple span beams with three, five, seven, and nine stiffness nodes and a single crack were modeled in this work. To confirm that the ANN approach can characterize the logic within the databases, networks with geometric, material, and cracking parameters as inputs and stiffness values as outputs were created. These networks provided excellent prediction accuracy measures ([Math Processing Error]). For the inverse problem, the noted trend shows that better prediction accuracy measures are achieved when more stiffness nodes are used in the ANN modeling process. It was also observed that providing some outputs to the ANN as inputs, thus decreasing the number of required outputs, immensely improves the quality of predictions provided by the ANN. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EM.1943-7889.0000536