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AbouRizk, S M, Halpin, D W and Wilson, J R (1991) Visual Interactive Fitting of Beta Distributions. Journal of Construction Engineering and Management, 117(04), 589–605.
Abudayyeh, O Y and Rasdof, W J (1991) Design of Construction Industry Information Management Systems. Journal of Construction Engineering and Management, 117(04), 698–715.
Bernold, L E and Treseler, J F (1991) Vendor Analysis for Best Buy in Construction. Journal of Construction Engineering and Management, 117(04), 645–58.
Cole, L J R (1991) Construction Scheduling: Principles, Practices, and Six Case Studies. Journal of Construction Engineering and Management, 117(04), 579–88.
De La Garza, J M and Mitropoulos, P (1991) . Journal of Construction Engineering and Management, 117(04), 736–55.
De La Garza, J M, Vorster, M C and Parvin, C M (1991) Total Float Traded as Commodity. Journal of Construction Engineering and Management, 117(04), 716–27.
de Neufville, R and King, D (1991) Risk and Need‐for‐Work Premiums in Contractor Bidding. Journal of Construction Engineering and Management, 117(04), 659–73.
Kakoto, T and Skibniewski, M (1991) Engineering Decision Support of Automated Shield Tunneling. Journal of Construction Engineering and Management, 117(04), 674–90.
Moselhi, O, Hegazy, T and Fazio, P (1991) Neural Networks as Tools in Construction. Journal of Construction Engineering and Management, 117(04), 606–25.
- Type: Journal Article
- Keywords: Artificial intelligence; Decision making; Expert systems; Construction industry; Automation; Neural networks; Computer applications;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)0733-9364(1991)117:4(606)
- Abstract:
The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligence (AI), a branch of computer science, has provided more suitable tools to the construction industry. Expert systems have steadily been introduced for different applications in the industry. However, the performance of these systems during the last decade, is far from ideal. Neural networks research in AI has recently provided powerful systems that work as a supplement or a complement to such conventional expert systems. In this paper, neural networks are introduced as a promising management tool that can enhance current automation efforts in the construction industry, including expert systems applications. Basic neural network architectures are described, and their potential applications in construction engineering and management discussed. A neural network application is developed for optimum markup estimation. Future possibilities of integrating neural networks and expert systems as a basis for developing efficient intelligent systems are described.
Reinschmidt, K F, Griffis, F H ( and Bronner, P L (1991) Integration of Engineering, Design, and Construction. Journal of Construction Engineering and Management, 117(04), 756–72.
Riggs, L S and Hills, J W (1991) Implications for U.S. Construction Companies in 1992 European Community. Journal of Construction Engineering and Management, 117(04), 773–90.
Rowings, J E (1991) Project‐Controls Systems Opportunities. Journal of Construction Engineering and Management, 117(04), 691–7.
Sanders, S R and Thomas, H R (1991) Factors Affecting Masonry‐Labor Productivity. Journal of Construction Engineering and Management, 117(04), 626–44.
Touran, A (1991) Modeling Uncertainty in Operations with Nonstationary Cycle Times. Journal of Construction Engineering and Management, 117(04), 728–35.