Applying the Hybrid Model to Optimize the Construction Cost index

Abstract

Construction usually involves risks and uncertainty since its complicated operations require huge capital, long time, and intensive labor resources. The cost of construction is always a major concern of owners and contractors. Thus, the ability to accurately forecast future trends in the Construction Cost Index (CCI) is critical for construction cost managers in order to prepare accurate budgets and proper bids. However, CCI forecasting accuracy is affected by simultaneous fluctuations in many factors (e.g., domestic economic conditions, supplies and equipment expenses, gasoline prices, or economic indicators). The main contribution of this research is a hybrid model using an artificial neural network optimized by the Particle Swarm algorithm intelligence to help cost engineers deal with the variability of CCI. A stratified 10-fold cross-validation method was adopted to compare the average performance of the models. This research uses 17 potentially significant factors and 50 CCI data set were used to build the proposed model. To increase the objectivity of the study, the employed Algorithm accuracy is the average accuracy of the ten models in ten validation rounds. This study is a useful proposal for cost engineers and project managers to draw up more accurate budgets, to offer reasonable prices to reduce construction costs during the operation process.