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Proceedings Paper

Automated real-time pavement distress detection using fuzzy logic and neural network
Author(s): Heng-Da Cheng
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Paper Abstract

Conventional visual and manual pavement distress analysis approaches are very costly, time-consuming, dangerous, labor-intensive, tedious, subjective, having high degree of variability, unable to provide meaningful quantitative information, and almost always leading to inconsistencies in distress detail over space and across evaluations. In this paper, a novel system for multipurpose automated real-time pavement distress analysis based on fuzzy logic and neural networks will be studied. The proposed system can: provide high data acquisition rates; effectively and accurately identify the type, severity and extent of surface distress; improve the safety and efficiency of data collection; offer an objective standard of analysis and classification of distress; help identify cost effective maintenance and repair plans; provide images and examples through information highway to other user/researchers; provide image/sample back for training or as the benchmark for testing new algorithms. The proposed system will reduce the cost for maintenance/repair greatly, and can contribute to other research in pavement maintenance, repair and rehabilitation.

Paper Details

Date Published: 13 November 1996
PDF: 12 pages
Proc. SPIE 2946, Nondestructive Evaluation of Bridges and Highways, (13 November 1996); doi: 10.1117/12.259131
Show Author Affiliations
Heng-Da Cheng, Utah State Univ. (United States)

Published in SPIE Proceedings Vol. 2946:
Nondestructive Evaluation of Bridges and Highways
Steven B. Chase, Editor(s)

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