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Alroomi, A, Jeong, D H S and Oberlender, G D (2012) Analysis of Cost-Estimating Competencies Using Criticality Matrix and Factor Analysis. Journal of Construction Engineering and Management, 138(11), 1270–80.
Chotibhongs, R and Arditi, D (2012) Detection of Collusive Behavior. Journal of Construction Engineering and Management, 138(11), 1251–8.
De Marco, A, Mangano, G, Corinna Cagliano, A and Grimaldi, S (2012) Public Financing into Build-Operate-Transfer Hospital Projects in Italy. Journal of Construction Engineering and Management, 138(11), 1294–302.
Hwang, S, Park, M, Lee, H and Kim, H (2012) Automated Time-Series Cost Forecasting System for Construction Materials. Journal of Construction Engineering and Management, 138(11), 1259–69.
- Type: Journal Article
- Keywords: Auto-regressive moving-average models; Construction costs; Predictions; Time series analysis; Construction materials; Autoregressive moving-average models; Construction costs; Pricing; Predictions; Time-series analysis;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000536
- Abstract:
As large-scale building projects increase in frequency, their construction costs become a matter of great concern, especially because of their lengthy construction periods. In particular, recent volatile fluctuations of construction material prices have fueled problems like cost forecasting. Many researchers try to accurately estimate cost escalations, but price forecasting for numerous construction materials requires a simplified and automated process. The research in this paper develops an automated time-series material cost forecasting (ATMF) system including both autoselected procedures for determining a best-fitting model and an autoextracting module for forecasting values using the Box-Jenkins approach. If the modeling process is simplified and iterative arbitrary decisions for the modeler eliminated, each future prices of a large number of materials can be forecast differently. Thus, the ATMF system can be utilized for predicting future trends in construction material costs. Further, an out-of-sample forecast applying several material price data confirms that this system can be effectively applied to material cost estimation at a more detailed level in object-based cost planning. The proposed system can thus help decision makers in the construction industry deal with changes in economic conditions and design by estimating cost escalations caused by volatile factors such as inflation.
Kim, B, Lee, H, Park, H and Kim, H (2012) Framework for Estimating Greenhouse Gas Emissions Due to Asphalt Pavement Construction. Journal of Construction Engineering and Management, 138(11), 1312–21.
Lee, G and Kim, S (2012) Case Study of Mass Customization of Double-Curved Metal Façade Panels Using a New Hybrid Sheet Metal Processing Technique. Journal of Construction Engineering and Management, 138(11), 1322–30.
Pan, W, Dainty, A R J and Gibb, A G F (2012) Establishing and Weighting Decision Criteria for Building System Selection in Housing Construction. Journal of Construction Engineering and Management, 138(11), 1239–50.
Pan, W, Gibb, A G F and Dainty, A R J (2012) Strategies for Integrating the Use of Off-Site Production Technologies in House Building. Journal of Construction Engineering and Management, 138(11), 1331–40.
Shapira, A, Simcha, M and Goldenberg, M (2012) Integrative Model for Quantitative Evaluation of Safety on Construction Sites with Tower Cranes. Journal of Construction Engineering and Management, 138(11), 1281–93.
Xiang, P, Zhou, J, Zhou, X and Ye, K (2012) Construction Project Risk Management Based on the View of Asymmetric Information. Journal of Construction Engineering and Management, 138(11), 1303–11.
Zhang, L and Qi, J (2012) Controlling Path and Controlling Segment Analysis in Repetitive Scheduling Method. Journal of Construction Engineering and Management, 138(11), 1341–5.