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Al-Sudairi, A A and Al-Motairi, M S (2010) Multi objective land use allocation model using priority-based goal programming technique. Construction Management and Economics, 28(02), 61.

Anagnostopoulos, K P and Koulinas, G K (2010) A simulated annealing hyperheuristic for construction resource levelling. Construction Management and Economics, 28(02), 75.

Bologna, R and Nord, R D (2000) Effects of the law reforming public works contracts on the Italian building process. Building Research & Information, 28(02), 109–18.

Bremer, W and Kok, K (2000) The Dutch construction industry: a combination of competition and corporatism. Building Research & Information, 28(02), 98–108.

Campagnac, E (2000) The contracting system in the French construction industry: actors and institutions. Building Research & Information, 28(02), 131–40.

Loosemore, M, Phua, F, Dunn, K and Ozguc, U (2010) Operatives’ experiences of cultural diversity on Australian construction sites. Construction Management and Economics, 28(02), 88.

Lu, S-L and Sexton, M (2010) Career journeys and turning points of senior female managers in small construction firms. Construction Management and Economics, 28(02), 39.

McGuffin, A A and Obonyo, E (2010) Enhancing performance: a case study of the effects of employee coaching in construction practice. Construction Management and Economics, 28(02), 9.

Ökmen, Ö and Öztaş, A (2010) Construction cost analysis under uncertainty with correlated cost risk analysis model. Construction Management and Economics, 28(02), 12.

  • Type: Journal Article
  • Keywords: cost modelling; uncertainty; risk management; risk analysis; correlation; simulation
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446190903468923
  • Abstract:
    Cost estimation is an important task in construction projects. Since various risk-factors affect the construction costs, the actual costs generally deviate from the estimated costs in a favourable or an adverse direction. Therefore, not only estimation of the costs but also an analysis of the uncertainty of the estimated costs is required. This requirement gains more importance in projects constrained by money as the main driver. The traditional cost estimation, i.e. predicting the construction costs and simply calculating the total, is deterministic and insufficient. This approach neglects the uncertainty and the correlation effects. A new simulation-based model—the correlated cost risk analysis model (CCRAM)—is proposed to analyse the construction costs under uncertainty when the costs and risk-factors are correlated. CCRAM captures the correlation between the costs and risk-factors indirectly and qualitatively. The efficiency and effectiveness of the model is evaluated through an application of CCRAM and Monte Carlo simulation (MCS) based method using the same hypothetical data. The findings show that CCRAM operates well and produces more consistent results compatible with the theoretical expectancies.

Syben, G (2000) Contractors take command: from a demand-based towards a producer oriented model in German construction. Building Research & Information, 28(02), 119–30.

Tuuli, M M, Rowlinson, S and Koh, T Y (2010) Dynamics of control in construction project teams. Construction Management and Economics, 28(02), 202.

Winch, G M (2000) Institutional reform in British construction: partnering and private finance. Building Research & Information, 28(02), 141–55.

Ye, K, Shen, L and Tan, Y (2010) Response strategies to the competition in the Chinese construction market. Construction Management and Economics, 28(02), 24.