Combining case-based reasoning with genetic algorithm optimization for preliminary cost estimation in construction industry

Abstract

In this paper, we propose a hybrid case-based reasoning (CBR) system for predicting the construction cost of high-rise buildings at the preliminary design stage. First, the extracted cost factors (CFs) of a high-rise building were shown to significantly improve the cost estimation system’s performance. For developing a CBR system, a hybrid approach that combines CBR with genetic algorithms (GAs) for cost estimation was adopted. Genetic algorithms were used for optimized weight generation and applied to real project cases. Additionally, this paper proposes the identification of an alternative similarity score measurement formula. The proposed formula evaluates the contrast between the alternative case matching approach and the classical formula in a scenario involving the use of cost factors describing a case. The results indicate that the proposed GA-based CBR system can consistently reduce errors and potentially be useful to owners and contractors in the early financial planning stage. Accordingly, it is expected that the developed CBR system would provide decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling the cost.