Currently, assessment techniques are performed subjectively, time consuming, and rely mostly on human visual inspection.  Such subjective assessment methods have been identified as a critical obstacle to effective infrastructure management. These techniques have not taken advantage of advanced technologies, especially computer technology. Therefore, more objective, accurate, and reliable assessment techniques using advanced technologies need to be explored to improve the quality of infrastructure and constructed facilities.

In the next stage, which is the pre-processing stage, image analysis techniques are used to analyze and enhance the image by applying algorithms such as filtering and edge detection. The image pre-processing is used to obtain the numerical parameters of the image such as the gray level and brightness in a numerical format. At this stage, a statistical pattern recognition algorithm is utilized to identify defects according to the numerical representation of images.

The next stage is the Processing stage. After being trained, neural networks are used to identify defects in the images by assigning binary variable of 0 or 1 for each pixel in the image. During the network training, the neural network is fed with different images and their parameters such as the pixels' gray levels. The network is also fed mapped values of 0 or 1 for each pixel value. The network will learn to assign the binary variable 0 or 1 for different scenarios according to image parameters.

Currently, the applications of this system has been investigated in the School of Civil Engineering at Purdue University. Number of researches in this area are performed for specific applications in the field of steel bridges quality inspection and underground infrastructure assessments.