Techno Press
You logged in as Techno Press

Computers and Concrete
  Volume 3, Number 6, December 2006, pages 439-454

Artificial neural network model for the strength prediction of fully restrained RC slabs subjected to membrane action
Khandaker M. A. Hossain, Mohamed Lachemi and Said M. Easa

    This paper develops an artificial neural network (ANN) model for uniformly loaded restrained reinforced concrete (RC) slabs incorporating membrane action. The development of membrane action in RC slabs restrained against lateral displacements at the edges in buildings and bridge structures significantly increases their load carrying capacity. The benefits of compressive membrane action are usually not taken into account in currently available design methods based on yield-line theory. By extending the existing knowledge of compressive membrane action, it is possible to design slabs in building and bridge decks economically with less than normal reinforcement. The processes involved in the development of ANN model such as the creation of a database of test results from previous research studies, the selection of architecture of the network from extensive trial and error procedure, and the training and performance validation of the model are presented. The ANN model was found to predict accurately the ultimate strength of fully restrained RC slabs. The model also was able to incorporate strength enhancement of RC slabs due to membrane action as confirmed from a comparative study of experimental and yield line-based predictions. Practical applications of the developed ANN model in the design process of RC slabs are also highlighted.
Key Words
    artificial neural network; membrane action; reinforced concrete slab; ultimate strength, yield-line method.
Dept. of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2021 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: