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Burleson, R C, Haas, C T, Tucker, R L and Stanley, A (1998) Multiskilled Labor Utilization Strategies in Construction. Journal of Construction Engineering and Management, 124(06), 480–9.

Camfield, F E (1998) Guidelines for Quarrystone Armor Units. Journal of Construction Engineering and Management, 124(06), 465–6.

Diekmann, J E and Featherman, W D (1998) Assessing Cost Uncertainty: Lessons from Environmental Restoration Projects. Journal of Construction Engineering and Management, 124(06), 445–51.

Finke, M R (1998) A Better Way to Estimate and Mitigate Disruption. Journal of Construction Engineering and Management, 124(06), 490–7.

Harper, R S and Koehn, E (1998) Managing Industrial Construction Safety in Southeast Texas. Journal of Construction Engineering and Management, 124(06), 452–7.

Kale, S and Arditi, D (1998) Business Failures: Liabilities of Newness, Adolescence, and Smallness. Journal of Construction Engineering and Management, 124(06), 458–64.

Konchar, M and Sanvido, V (1998) Comparison of U.S. Project Delivery Systems. Journal of Construction Engineering and Management, 124(06), 435–44.

Molenaar, K R and Songer, A D (1998) Model for Public Sector Design-Build Project Selection. Journal of Construction Engineering and Management, 124(06), 467–79.

  • Type: Journal Article
  • Keywords:
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(467)
  • Abstract:
    Public sector owners are rapidly identifying new construction procurement methods. Changing procurement laws and documented project success are encouraging owners to attempt the design-build method of project procurement. Design-build is a radical departure from the traditional design-bid-build method. This paper reports on the analysis of 122 case studies and the resulting automated tool for public sector design-build project selection. Prediction models are developed for five performance criteria that correlate specific project characteristics to success. Performance criteria and associated models include budget variance, schedule variance, conformance to expectations, administrative burden, and overall user satisfaction. Project characteristics are categorized as project, owner, market, and relationship variables. Statistically significant correlations with success include scope definition, schedule definition, budget definition, project complexity, agency experience, agency staffing, owner design input, design-build market, design-builder prequalification, and method of selection.

Sonmez, R and Rowings, J E (1998) Construction Labor Productivity Modeling with Neural Networks. Journal of Construction Engineering and Management, 124(06), 498–504.