Abstracts – Browse Results
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Abou-Zeid, A M K (1993) Data flow identification to support project automation/integration and productivity, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Al-Fares, M A (1995) On the development of an integrated system for the automation of on-site reinforced concrete construction, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Ammar, A I (2001) Profitability of electrical contractors using financial and economical data: The effect of company's size, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Choi, J (2003) Strategic motives underlying M and A transactions in the United States public construction industry, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Choi, K (2008) Applications of its archived data for improved highway planning and design, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Huang, C-N (2003) Time study on two-echelon supply chain for steel framing construction: Field investigation and networking model simulation, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Ibrahim, M W W (2018) Improving project performance by mitigating out-of-sequence work and assessing construction readiness, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Jang, H (2002) Construction material logistics: Project manager level of satisfaction and facilities layout planning, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Kang, M M K (2014) Development of an expert system for sustainable pavement preservation strategies (es2p2s), Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Kim, S-K (2001) Toward a framework for an intelligent earthwork system (ies), Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Koo, K-J (2000) Organizational program management for multiple maintenance projects under multi-trade capacity constraints, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Lee, J C (2010) Evaluating the sustainability of construction with recycled materials, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Lee, S (2000) Spatial model and decentralized path planning for construction automation, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Lukito, P K (2000) A framework for environmental monitoring infrastructure investment planning in developing areas, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Rungjang, K (2013) Public investment for developing port facilities within competitive market, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Schwab, A (2000) Management practices in short-term network organizations: The performance impact of the shadow of the future and psychological contracts in the United States movie industry, 1931--1940, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Shr, J-F (1999) Model development for cost-plus-time bidding applied to Florida department of transportation highway construction, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Sullivan, K T (2004) Quantification of the cumulative impact of change orders on sheet metal labor productivity, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
Taha, M A-E (1994) Applying distributed artificial intelligence to the prequalification of construction contractors, Unpublished PhD Thesis, , The University of Wisconsin - Madison.
- Type: Thesis
- Keywords: complexity; decision support; artificial intelligence; decision making; learning; prequalification; problem solving; owner; construction contractor; neural network
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/304144946
- Abstract:
The construction industry has been criticized, to a large extent, for its slow acceptance and use of technological improvements to plan and execute projects. The changing global environment and the increasing complexity of the industry has created a need for adopting advanced technologies. Computer-based technologies such as artificial intelligence techniques are generating interest as potential aids for decision making in different engineering and management decision domains. Knowledge-based systems have steadily been introduced for different applications in the construction industry. Most of the knowledge-based system applications currently available for the construction industry can be described as a single agent system in which a single body of knowledge is used to solve the entire problem. Such systems require intensive software development and maintenance in addition to large allocated memory. They also lack the ability to learn by themselves, generalize solutions and adequately respond to highly correlated, noisy or previously unseen data. Moreover, single agent systems are not adequate for solving human problems that usually requires the involvement of multiple decision makers. This dissertation illustrates the capabilities of distributed artificial intelligence in representing and using knowledge in the construction industry. The objective of this study is two-fold: (1) to introduce a methodology, that uses the distributed artificial intelligence capabilities, to develop adaptive DSS for solving construction industry problems and (2) to develop an adaptive computerized tool for performing owner-contractor prequalification. These objectives were achieved through the development of the Contractor Selector Decision Support System, CONSEL, an adaptive DSS for prequalifying construction contractors. The problem solving strategy of the developed DSS was distributed among eight problem solvers to mimic the actual prequalification procedure that involves several tasks. Different problem solvers of the system were developed by using the learning capabilities of neural networks and production rules. The distributed artificial intelligence architecture suits the area of contractor prequalification in which several tasks are examined and integrated to arrive at the final decision. A learning subsystem is included to modify its problem solving knowledge through experience. These learning capabilities will constantly improve the system's performance.