Intelligent Model for Constructed Facilities Surface Assessment
Publication: Journal of Construction Engineering and Management
Volume 126, Issue 6
Abstract
Computerized intelligent systems can simulate human expertise as well as analyze and process vast amounts of data instantaneously. This paper presents a hybrid intelligent computerized model for constructed facilities surface quality assessment. The model uses computers to analyze digital images of the areas to be assessed to identify and measure defects. Moreover, neural networks are used to train the system to automate the process and replicate the experts' knowledge in identifying the defects. Most techniques, currently used in construction quality assessment, rely mostly on subjective criteria. The model applies digital image processing and neural network techniques for constructed facilities surface quality assessment to make the process objective, quantitative, consistent, and reliable. Highway steel bridge coating assessment was used to exemplify the generic model.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
AbdelRazig, Y. ( 1999). “Construction quality assessment: A hybrid decision support model using image processing and neural learning for intelligent defects recognition.” PhD thesis, Purdue University, West Lafayette, Ind.
2.
Abraham, D., Iseley, T., Prasanth, R., and Wirahadikusumah, R. ( 1997). “Integrating sensing technologies for underground utilities assessment.” Proc., Fac. Mgmt. Comp. Conf. on Infrastruct. Condition Assessment: Art, Sci., and Pract., Urban Transp. Div., ASCE, New York, 316–325.
3.
Artificial neural networks for civil engineers: Fundamentals and applications. (1997). ASCE, New York, 19–43.
4.
Chang, L., and Hsie, M. (1995). “Developing acceptance-sampling methods for quality construction.”J. Constr. Engrg. and Mgmt., ASCE, 121(2), 246–253.
5.
Croall, I. F., and Mason, J. P. ( 1992). Industrial applications of neural networks, Springer, New York.
6.
Haykin, S. ( 1999). Neural networks: A comprehensive foundation, Prentice-Hall, Upper Saddle River, N.J.
7.
Hsie, M. ( 1994). “Computer aided acceptance planning: Generating acceptance parameters and stratified sampling plans through neural network learning ability and CAD modeling.” PhD thesis, Purdue University, West Lafayette, Ind.
8.
Hunt, V., Helmicki, A., and Aktan, E. ( 1997). “Instrumented monitoring and nondestructive evaluation of highway bridges.” Proc., Fac. Mgmt. Com. Conf. on Infrastruct. Condition Assessment: Art, Sci., and Pract., Urban Transp. Div., ASCE, New York, 121–130.
9.
Looney, C. ( 1997). Pattern recognition using neural networks, Oxford University Press, New York.
10.
Manual of professional practice: Quality in the constructed project: A guideline for owners, designers, and constructors. (1988). ASCE, New York, 17–22.
11.
Montgomery, D. ( 1991). Statistical quality control, Wiley, New York.
12.
Russ, J. C. ( 1995). The image processing handbook, CRC, Boca Raton, Fla.
13.
Shubinsky, G. ( 1994). “Application of optical imaging method for bridge maintenance and inspection.” ITI Tech. Rep. No. 4, Northwestern University, Evanston, Ill.
14.
Steel structures painting manual. (1989). Good painting practice, Steel Structures Painting Council, Vol. 1, 280–291, 490–519.
15.
Tsoukalas, H., and Uhrig, E. ( 1997). Fuzzy and neural approaches in engineering, Wiley, New York.
16.
Weeks, A. ( 1996). Fundamentals of electronic image processing, SPIE Optical Engineering Press, Bellingham, Wash., and IEEE Press, Piscataway, N.J.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 126 • Issue 6 • December 2000
Pages: 422 - 432
History
Received: Feb 24, 2000
Published online: Dec 1, 2000
Published in print: Dec 2000
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.