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Bayraktar, M E, Hastak, M, Gokhale, S and Safi, B (2011) Decision Tool for Selecting the Optimal Techniques for Cost and Schedule Reduction in Capital Projects. Journal of Construction Engineering and Management, 137(09), 645–55.

Commuri, S, Mai, A T and Zaman, M (2011) Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes. Journal of Construction Engineering and Management, 137(09), 634–44.

Dai, J and Goodrum, P M (2011) Differences in Perspectives regarding Labor Productivity between Spanish- and English-Speaking Craft Workers. Journal of Construction Engineering and Management, 137(09), 689–97.

González, V, Alarcón, L F, Maturana, S and Bustamante, J A (2011) Site Management of Work-in-Process Buffers to Enhance Project Performance Using the Reliable Commitment Model: Case Study. Journal of Construction Engineering and Management, 137(09), 707–15.

Goodrum, P M, Haas, C T, Caldas, C, Zhai, D, Yeiser, J and Homm, D (2011) Model to Predict the Impact of a Technology on Construction Productivity. Journal of Construction Engineering and Management, 137(09), 678–88.

Hwang, S (2011) Time Series Models for Forecasting Construction Costs Using Time Series Indexes. Journal of Construction Engineering and Management, 137(09), 656–62.

  • Type: Journal Article
  • Keywords: Construction costs; Time series analysis; Forecasting; Predictions; Models; Construction costs; Time series analysis; Forecasting models; Continual prediction;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000350
  • Abstract:
    Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.

Lin, G, Shen, G Q, Sun, M and Kelly, J (2011) Identification of Key Performance Indicators for Measuring the Performance of Value Management Studies in Construction. Journal of Construction Engineering and Management, 137(09), 698–706.

Wambeke, B W, Hsiang, S M and Liu, M (2011) Causes of Variation in Construction Project Task Starting Times and Duration. Journal of Construction Engineering and Management, 137(09), 663–77.