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Drejer, I and Vinding, A L (2006) Organisation, "anchoring" of knowledge, and innovative activity in construction. Construction Management and Economics, 24(09), 921-31.

Jha, K N and Iyer, C K (2006) What attributes should a project coordinator possess?. Construction Management and Economics, 24(09), 977-88.

Kim, H J and Reinschmidt, K F (2006) A dynamic competition model for construction contractors. Construction Management and Economics, 24(09), 955-65.

Manley, K and Mcfallan, S (2006) Exploring the drivers of firm-level innovation in the construction industry. Construction Management and Economics, 24(09), 911-20.

Pun, S K, Liu, C and Langston, C (2006) Case study of demolition costs of residential buildings. Construction Management and Economics, 24(09), 967-76.

Stoy, C and Kalusche, W (2006) The determination of occupancy costs during early project phases. Construction Management and Economics, 24(09), 933-44.

Whitla, P, Walters, P and Davies, H (2006) The use of global strategies by British construction firms. Construction Management and Economics, 24(09), 945-54.

Yang, I-T (2006) Using Gaussian copula to simulate repetitive projects. Construction Management and Economics, 24(09), 901-9.

  • Type: Journal Article
  • Keywords: Copula; simulation; stochastic estimation; risk management; repetitive project
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446190600658784
  • Abstract:

    An important requirement for simulating repetitive projects is to treat correlations inherent in the repetition of same crews working at various locations. To attain the requirement, this study develops a new Monte Carlo simulation model implementing a Gaussian copula in conjunction with the inverse-transform method to generate correlated duration samples in repetitive projects that have pre-specified marginal distributions and pairwise rank correlations. The proposed model is equipped with an automatic approximation procedure to adjust an infeasible correlation matrix, if necessary. The proposed model is statistically verified through a real-life residential apartment project. The simulation results are compared to two conventional analyses (PERT and simulation without correlation) to show the aggregated impact of correlations. The proposed model contributes to the state-of-the-art in handling non-linear dependencies among activity durations that may have non-normal distributions. Moreover, it is flexible in the ways of correlation assessments (qualitative or quantitative), the magnitudes of correlations (weak to strong), and the types of marginal distributions (symmetrical or skewed).