Dynamic Neural Network Predicting by Abed Mustafa (6 results)

Taschenbuch. Condition: Neu. Dynamic Neural Network for Predicting Creep of Structural Masonry | An Application of Artificial Intelligence Techniques | Mustafa Mohammed Abed | Taschenbuch | 96 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783846588208 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the inherent modeling problems in structural engineering is creep of quasi-brittle materials (e.g., concrete and masonry). The creep strain represents the non-instantaneous strain that occurs with time when the stress is sustained. Several creep models with limited accuracy have been developed within the last few decades to predict creep of concrete and masonry structures. The stochastic nature of creep deformation and its reliance on a large number of uncontrolled parameters (e.g., relative humidity, age of loading, stress level) makes the process of prediction difficult, and yet accurate mathematical model almost impossible. This study investigates the potential use of Dynamic Neural Network (DNN) for predicting creep of structural masonry. The main motive of use DNN is that DNN could memorize the sequential or time-varying patterns while training process. Thus, DNN becomes more capable of capturing the time-dependent of creep deformation than the static networks. The results showed that the developed DNN models are able to predict the creep deformation with an excellent level of accuracy compared with that of conventional methods and the static networks models. 96 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - One of the inherent modeling problems in structural engineering is creep of quasi-brittle materials (e.g., concrete and masonry). The creep strain represents the non-instantaneous strain that occurs with time when the stress is sustained. Several creep models with limited accuracy have been developed within the last few decades to predict creep of concrete and masonry structures. The stochastic nature of creep deformation and its reliance on a large number of uncontrolled parameters (e.g., relative humidity, age of loading, stress level) makes the process of prediction difficult, and yet accurate mathematical model almost impossible. This study investigates the potential use of Dynamic Neural Network (DNN) for predicting creep of structural masonry. The main motive of use DNN is that DNN could memorize the sequential or time-varying patterns while training process. Thus, DNN becomes more capable of capturing the time-dependent of creep deformation than the static networks. The results showed that the developed DNN models are able to predict the creep deformation with an excellent level of accuracy compared with that of conventional methods and the static networks models.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abed Mustafa MohammedMustafa Mohammed Abed has obtained his M.Sc degree in Civil and Structural Engineering from National University of Malaysia in 2011. He has worked as a Consultant Engineer in different international projects in t.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -One of the inherent modeling problems in structural engineering is creep of quasi-brittle materials (e.g., concrete and masonry). The creep strain represents the non-instantaneous strain that occurs with time when the stress is sustained. Several creep models with limited accuracy have been developed within the last few decades to predict creep of concrete and masonry structures. The stochastic nature of creep deformation and its reliance on a large number of uncontrolled parameters (e.g., relative humidity, age of loading, stress level) makes the process of prediction difficult, and yet accurate mathematical model almost impossible. This study investigates the potential use of Dynamic Neural Network (DNN) for predicting creep of structural masonry. The main motive of use DNN is that DNN could memorize the sequential or time-varying patterns while training process. Thus, DNN becomes more capable of capturing the time-dependent of creep deformation than the static networks. The results showed that the developed DNN models are able to predict the creep deformation with an excellent level of accuracy compared with that of conventional methods and the static networks models.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.…