Rapid analysis of externally reinforced concrete beams using neural networks

The load-carrying capacity of reinforced concrete beams can be compromised by concrete cracking, and the intrusion of moisture, oxygen and salt that cause corrosion of the steel reinforcement. A relatively inexpensive method of repairing such beams is to bond fiber-reinforced composites to the tensile and shear faces of the beams. Unfortunately, the numeric tools used to analyze such beams (notably, finite element analysis (FEM)) are computationally expensive making them slow to arrive at an answer, especially when dealing with complicated three-dimensional composite forms. An empirical solution is therefore proposed that involves the development of a neural network model of the performance of externally reinforced beams, developed from laboratory observations of actual beam behavior.