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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.

  • Type: Journal Article
  • Keywords: Construction management; Productivity; Technology; Validation; Models; Construction productivity; Technology; Predictive model; Validation;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000328
  • Abstract:
    Although some new technologies promise to improve construction productivity, their ability to deliver is not always realized. Building on a great deal of prior research, a four-stage predictive model was developed and validated to estimate the potential for a technology to have a positive impact on construction productivity. The four stages examine the costs, feasibility, usage history, and technical impact of a technology. The predictive model combines results from historical analyses to formalize how selected technologies with improved construction productivity can be used as a predictor of how future technologies might do the same. Each of the stages of a predictive model was subdivided into a series of categories and questions, which were weighted by importance by using the analytic hierarchy process and historical analysis to generate a performance score for the analyzed technology. The predictive model was then validated by using 74 previous and existing construction technologies. Statistical analysis confirmed that average performance scores produced by the model were significantly different across the categories of successful, inconclusive, and unsuccessful in the actual implementation experience of technologies.

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

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.