June 16, 2002
June 16, 2002
June 19, 2002
7.164.1 - 7.164.8
Main Menu Session 2306
An Artificial Neural Network Model for Preliminary Design of Reinforced Concrete Beam-Column
Mohammed E. Haque, Ph.D., P.E.
Texas A&M University
The dimensions of a beam-column cross-section and the area of reinforcing steel required to support a specific combination of axial load and moment can be established by using the column design interaction curves, where an interaction curve represents all possible combinations of axial load and moment that produce failure of the cross-section. The bending resistance of an axially loaded column about a particular skewed axis due to biaxial moments can be determined through iterations and lengthy calculations. These extensive calculations are multiplied when optimization of the reinforcing steel or column cross-section is required. This paper investigated the suitability of an Artificial Neural Network (ANN) for modeling a preliminary design of reinforced concrete beam-column. Neural computing is a relatively new field of artificial intelligence (AI), which tries to mimic the structure and operation of biological neural systems, such as the human brain, by creating an ANN on a computer. An ANN back-propagation multi- layered model was developed to design a beam-column, which predicted column cross-section for a given set of inputs, which were concrete compressive strength, column types (Tied and Spiral), reinforcing steel ratio (0.01 – 0.08), factored axial load, Pu, and moment, Mu. In the present research, several different ANN back-propagation trial models with different layers/slabs connections, weights and activation functions were trained. The presented back-propagation multi-layered neural net with logistic activation function, "Rotation" for pattern selection, and "TurboProp" for weight updates was the best one among all other trials, which converged very rapidly to reach an excellent statistical performance. The trained ANN back-propagation model was tested with several actual design data, and a comparative evaluation between the ANN model predictions and the actual designs was presented.
Artificial neural network (ANN) is one of the artificial intelligence algorithms that relates to the class of machine learning. It mimics a human brain process of acquiring and retrieving knowledge. It models the biological neuron, which consists of nodes (cells) and links (axon). It is defined as "A computing system made up of a number of simple, highly interconnected processing elements, which processes information by its dynamic state response to external input1. These ANNs are modeling techniques that are especially useful to address problems where solutions are not clearly formulated2 or where the relationships between inputs and outputs are not sufficiently known. ANNs have the ability to learn by example. Patterns in a
Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition Copyright © 2002, American Society for Engineering Education
Haque, M. (2002, June), An Artificial Neural Network Model For Preliminary Design Of Reinforced Concrete Beam Column Paper presented at 2002 Annual Conference, Montreal, Canada. https://peer.asee.org/10929
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2002 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015