COMPARISON OF ANN AND CBR MODELS FOR EARLY COST PREDICTION OF STRUCTURAL SYSTEMS
Reasonably accurate cost estimation of the structural system is quite desirable at the early stages of the design process of a construction project. However, the numerous interactions among the many cost-variables make the prediction difficult. Artificial neural networks (ANN) and case-based reasoning (CBR) are reported to overcome this difficulty. This paper presents a comparison of CBR and ANN augmented by genetic algorithms (GA) conducted by using spreadsheet simulations. GA was used to determine the optimum weights for the ANN and CBR models. The cost data of twenty-nine actual cases of residential building projects were used as an example application. Two different sets of cases were randomly selected from the data set for training and testing purposes. Prediction rates of 84% in the GA/CBR study and 89% in the GA/ANN study were obtained. The advantages and disadvantages of the two approaches are discussed in the light of the experiments and the findings. It appears that GA/ANN is a more suitable model for this example of cost estimation where the prediction of numerical values is required and only a limited number of cases exist. The integration of GA into CBR and ANN in a spreadsheet format is likely to improve the prediction rates.