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Lam, E W, Chan, A P and Chan, D W (2008) Determinants of Successful Design-Build Projects. Journal of Construction Engineering and Management, 134(05), 333–41.

Mao, X and Zhang, X (2008) Construction Process Reengineering by Integrating Lean Principles and Computer Simulation Techniques. Journal of Construction Engineering and Management, 134(05), 371–81.

Ozorhon, B, Arditi, D, Dikmen, I and Birgonul, M T (2008) Implications of Culture in the Performance of International Construction Joint Ventures. Journal of Construction Engineering and Management, 134(05), 361–70.

Shapira, A, Rosenfeld, Y and Mizrahi, I (2008) Vision System for Tower Cranes. Journal of Construction Engineering and Management, 134(05), 320–32.

Yang, I (2008) Distribution-Free Monte Carlo Simulation: Premise and Refinement. Journal of Construction Engineering and Management, 134(05), 352–60.

Yousefi, S, Hegazy, T, Capuruço, R A and Attalla, M (2008) System of Multiple ANNs for Online Planning of Numerous Building Improvements. Journal of Construction Engineering and Management, 134(05), 342–51.

  • Type: Journal Article
  • Keywords: Estimation; Scheduling; Computer applications; Reconstruction; Maintenance costs; Infrastructure;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:5(342)
  • Abstract:
    The aging infrastructure in North America and worldwide mandates large investments in repair and improvement (R&I) activities. For organizations that own many assets, managing a large number of R&I activities is not a simple task and requires accurate estimating and scheduling so that proper budgeting and resource allocation decisions can be made. To support these decisions, this paper introduces a Web-based system that estimates the cost and duration of a user-requested R&I activity and provides alternative schedules based on resource availability. For estimating, the Web-based system hosts 32 artificial neural networks (ANNs), trained on actual historical data, for 32 common R&I activities in building projects. Each ANN incorporates a sensitivity analysis to consider the uncertainty in the input parameters on the estimate, and is linked to a central scheduling algorithm for resource allocation based on a first-come first-serve basis. The automated system helps practitioners in planning numerous R&I requests with least time, cost, and paper work. Details on system development are provided in this paper along with perceived benefits and the opinion of users on its performance.

Zhang, C, Zayed, T and Hammad, A (2008) Resource Management of Bridge Deck Rehabilitation: Jacques Cartier Bridge Case Study. Journal of Construction Engineering and Management, 134(05), 311–9.