Preliminary quantity estimate of highway bridges using neural networks

Authors:
G. Morcous;M. M. Bakhoum;M. A. Taha;M. El-Said
Affiliations:
Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada;Department of Structural Engineering, Cairo University, Giza, Egypt;Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada;Department of Structural Engineering, Cairo University, Giza, Egypt
Venue:
ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
Year:
2001

Citing 2
Cited 1

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Abstract

Current models for preliminary quantity estimate of highway bridges are not successful in preparing a quick and economic bill of quantity with sufficient accuracy for bidding purposes. Moreover, most of these models have serious problems in their development and update. In this paper, an effort to develop an Artificial Neural Network (ANN) model that eliminates the shortcomings of the current models is presented. An ANN with back-propagation learning algorithm was trained to estimate the concrete volume and prestressing weight in bridge superstructure. A set of 22 prestressed concrete bridges constructed in Egypt over the Nile was used in training and testing the network. Testing results indicated that ANNs are sufficient tools for the preliminary quantity estimate of highway bridges.