A learning model for design-build project selection in the public sector

Abstract

The primary method of public sector project delivery in the United States (U.S.) has traditionally been design-bid-build delivery. The public sector has historically separated design and construction contracts. In the 1990s, the U.S. public sector began to experiment with design-build project delivery, which combines design and construction in one contract. In 1997, a decision support system was developed to provide a formal selection model for public sector design-build projects. The model supports public owners in determining which projects are appropriate for design-build delivery. This initial model was static in nature and was based on a regression analysis of 104 projects. The analysis resulted in a predictive model with five performance criteria: overall satisfaction; administrative burden; conformance to expectations; schedule variance; and budget variance. Since 1997, the number of design-build projects has increased dramatically and public sector design-build methods have evolved. The original model can be improved with new data and a new framework to provide for an adaptive model as the industry continues to evolve. This paper presents a formalized application and use of learning capabilities to supplement the original static model. This model adjusts parameters and functions using artificial intelligence as the main knowledge engine. This approach can be adapted to many applications of decision support in the design and construction industry.