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Response analysis of field-scale fully grouted standard cable bolts using a coupled ANN–FDM approach / Roozbeh Grayeli in Journal of engineering mechanics, Vol. 135 N° 6 (Juin 2009) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 135 N° 6 (Juin 2009) . - pp. 561-570 Titre : Response analysis of field-scale fully grouted standard cable bolts using a coupled ANN–FDM approach Type de document : texte imprimé Auteurs : Roozbeh Grayeli, Auteur ; Kianoosh Hatami, Auteur Article en page(s) : pp. 561-570 Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Neural  networks  Finite  difference  method  Bolts  Rocks  Axial  loads  Stabilization  Pull-out  resistance. Résumé : This paper presents a coupled approach using an artificial neural network (ANN) and the finite difference method (FDM) that has been developed to predict the distribution of axial load along fully grouted standard cable bolts in the field using laboratory pullout test data. A back-propagation training algorithm was used in ANN to determine axial loads in the cables tested in the laboratory. The ANN component of the computational model was trained using two different types of data sets. At first, the ANN was trained to predict the axial loads in a series of short cables grouted with Portland cement at a specific water-to-cement ratio and subjected to different radial confining stiffness values. Next, the ANN model was trained for an expanded case to include the influence of lateral confining stress on the distribution of axial load in the cable reinforcement. Finally, the ANN model was implemented into a widely used, FDM-based geotechnical software (FLAC). The accuracy of the ANN–FDM model is demonstrated in this paper against measured data from laboratory and field tests. The analysis approach introduced in this study is a valuable computational tool that can be used to determine the axial load distribution in long standard cable bolts, which are commonly installed to stabilize rock masses in various geotechnical, transportation, and mining applications. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JENMDT000 [...] [article] Response analysis of field-scale fully grouted standard cable bolts using a coupled ANN–FDM approach [texte imprimé] / Roozbeh Grayeli, Auteur ; Kianoosh Hatami, Auteur . - pp. 561-570. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 135 N° 6 (Juin 2009) . - pp. 561-570 Mots-clés : Neural  networks  Finite  difference  method  Bolts  Rocks  Axial  loads  Stabilization  Pull-out  resistance. Résumé : This paper presents a coupled approach using an artificial neural network (ANN) and the finite difference method (FDM) that has been developed to predict the distribution of axial load along fully grouted standard cable bolts in the field using laboratory pullout test data. A back-propagation training algorithm was used in ANN to determine axial loads in the cables tested in the laboratory. The ANN component of the computational model was trained using two different types of data sets. At first, the ANN was trained to predict the axial loads in a series of short cables grouted with Portland cement at a specific water-to-cement ratio and subjected to different radial confining stiffness values. Next, the ANN model was trained for an expanded case to include the influence of lateral confining stress on the distribution of axial load in the cable reinforcement. Finally, the ANN model was implemented into a widely used, FDM-based geotechnical software (FLAC). The accuracy of the ANN–FDM model is demonstrated in this paper against measured data from laboratory and field tests. The analysis approach introduced in this study is a valuable computational tool that can be used to determine the axial load distribution in long standard cable bolts, which are commonly installed to stabilize rock masses in various geotechnical, transportation, and mining applications. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JENMDT000 [...]