ARCOM

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Abdelgawad, M and Fayek, A R (2010) Risk Management in the Construction Industry Using Combined Fuzzy FMEA and Fuzzy AHP. Journal of Construction Engineering and Management, 136(09), 1028–36.

Adriaanse, A, Voordijk, H and Dewulf, G (2010) Adoption and Use of Interorganizational ICT in a Construction Project. Journal of Construction Engineering and Management, 136(09), 1003–14.

Bayraktar, M E and Hastak, M (2010) Scoring Approach to Construction Bond Underwriting. Journal of Construction Engineering and Management, 136(09), 957–67.

Hallowell, M R and Gambatese, J A (2010) Population and Initial Validation of a Formal Model for Construction Safety Risk Management. Journal of Construction Engineering and Management, 136(09), 981–90.

Hwang, S (2010) Cross-Validation of Short-Term Productivity Forecasting Methodologies. Journal of Construction Engineering and Management, 136(09), 1037–46.

Type: Journal Article
Keywords: Productivity; Forecasting; Estimation; Predictions; Time series analysis; Stochastic processes; Productivity; Forecasting; Estimates; Short-term prediction; Time series; Stochastic;
ISBN/ISSN: 0733-9364
URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000230
Abstract:
Frequent and regular prediction of productivity is needed to effectively manage construction operations in progress. This need is proven by significant deviations in productivity between estimated values and actual values, and the dynamic and stochastic changes in productivity over time. Five statistical methodologies that are appropriate for contemporaneous time series were cross validated in the present study. Validation was conducted by comparing the performance of forecasting models constructed using the methodologies. Performance was measured by evaluating the residual sum of squares and the correlation coefficients. As a result, univariate time series analysis was found to be the best-performing methodology. The univariate time series model is particularly beneficial in two respects. Using a single series of contemporaneous productivity data in numeric form, it reduces the effort expended in collecting and analyzing data, and improves the objectivity of analysis.

Hwang, S and Liu, L Y (2010) Contemporaneous Time Series and Forecasting Methodologies for Predicting Short-Term Productivity. Journal of Construction Engineering and Management, 136(09), 1047–55.

Ioannou, P G and Awwad, R E (2010) Below-Average Bidding Method. Journal of Construction Engineering and Management, 136(09), 936–46.

Love, P E D, Mistry, D and Davis, P R (2010) Price Competitive Alliance Projects: Identification of Success Factors for Public Clients. Journal of Construction Engineering and Management, 136(09), 947–56.

Sacks, R, Koskela, L, Dave, B A and Owen, R (2010) Interaction of Lean and Building Information Modeling in Construction. Journal of Construction Engineering and Management, 136(09), 968–80.

Serag, E, Oloufa, A, Malone, L and Radwan, E (2010) Model for Quantifying the Impact of Change Orders on Project Cost for U.S. Roadwork Construction. Journal of Construction Engineering and Management, 136(09), 1015–27.

Wong, J M W, Chan, A P C and Chiang, Y H (2010) Modeling Construction Occupational Demand: Case of Hong Kong. Journal of Construction Engineering and Management, 136(09), 991–1002.