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Cao, M, Cheng, M and Wu, Y (2015) Hybrid Computational Model for Forecasting Taiwan Construction Cost Index. Journal of Construction Engineering and Management, 141(04).
- Type: Journal Article
- Keywords: Multivariate adaptive regression spline; Radial basis function neural network; Artificial bee colony; Construction cost index; Cost estimate; Cost and schedule;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000948
The ability to accurately forecast future trends in the Construction Cost Index (CCI) is critical for construction cost managers to prepare accurate budgets for owners and prepare proper bids for contractors. However, CCI forecasting accuracy is affected by concurrent fluctuations in numerous factors (e.g., domestic/international economic conditions, economic indicators, and the price of energy). The main contribution of this study to the body of knowledge is the creation of a new procedure and a novel inference model, the self-adaptive structural radial basis neural network intelligence machine (SSRIM), to help cost engineers deal with the variability of CCI. In SSRIM, multivariate adaptive regression splines (MARS) analyzes the relative importance of various potential factors of influence on CCI, with those factors identified as significant assigned as input variables in the radial basis function neural network (RBFNN) and used to forecast CCI values. Meanwhile, the artificial bee colony (ABC) algorithm is employed to search for the optimal parameters of RBFNN to maximize the predictive accuracy of the model. A total of 122 Taiwan CCI data were used to build the proposed model, identifying SSRIM as the fittest CCI forecast model with attaining lowest values of RMSE and MAPE. It is expected that this work will contribute to the construction engineering and management global community by helping cost engineers and project managers prepare more accurate budget estimates, proper bids, and attain better-timed project execution to reduce construction costs during the operation process.