Comparison of Case-Based Reasoning and Artificial Neural Networks

Abstract

The outcome of construction litigation depends on a large number of factors. To predict the outcome of such litigation is difficult because of the complex interrelationships between these many factors. Two attempts are reported in the literature that use, respectively, case-based reasoning (CBR) and artificial neural networks (ANN) to overcome this difficulty. These studies were conducted by using the same 102 Illinois circuit court cases; an additional 12 cases were used for testing. Prediction rates of 83% in the CBR study and 67% in the ANN study were obtained. In this paper, CBR and ANN are compared, and their advantages and disadvantages are discussed in light of these two studies. It appears that CBR is more flexible when the system is updated with new cases, has better explanation facilities, and handles missing data and a large number of features better than ANN in this domain. If the use of CBR and ANN is understood better and if, as a result, the outcome of construction litigation can be predicted with reasonable accuracy and reliability, all parties involved in the construction process could save considerable money and time.